Friday Harbor LLC

United States of America

Back to Profile

1-30 of 30 for Friday Harbor LLC Sort by
Query
Aggregations
Jurisdiction
        United States 28
        World 2
IPC Class
G10L 25/90 - Pitch determination of speech signals 10
G10L 21/00 - Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility 5
G10L 21/0232 - Processing in the frequency domain 5
G10L 21/0264 - Noise filtering characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques 5
G10L 19/00 - Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis 4
See more
Found results for  patents

1.

Router path selection and creation in a single clock cycle

      
Application Number 15226833
Grant Number 09942146
Status In Force
Filing Date 2016-08-02
First Publication Date 2018-02-08
Grant Date 2018-04-10
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Florea, Michael
  • Coffin, Jerome V.

Abstract

Systems, devices, and techniques for routing packets are described. A described router includes ingress ports to receive packets; egress ports; ingress switch fabric coupled with the ingress ports; egress switch fabric coupled with the egress ports; floating buffers coupled between the ingress switch fabric and the egress switch fabric; and a controller. The controller can be configured to receive a packet via an ingress port, determine an egress port based on the packet's destination address, acquire a floating buffer, send to the egress port a buffer identifier corresponding to the acquired floating buffer, operate the ingress switch fabric to establish a first pathway between the acquired floating buffer and the ingress port to write the packet to the buffer, and operate the egress switch fabric to establish a second pathway between the acquired floating buffer and the egress port to write from the buffer to the egress port.

IPC Classes  ?

  • H04L 12/28 - Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
  • H04L 12/741 - Header address processing for routing, e.g. table lookup
  • H04L 12/935 - Switch interfaces, e.g. port details
  • H04L 12/879 - Single buffer operations, e.g. buffer pointers or buffer descriptors
  • H04L 12/933 - Switch core, e.g. crossbar, shared memory or shared medium

2.

MACHINE LEARNING AGGREGATION

      
Application Number US2017029904
Publication Number 2017/189879
Status In Force
Filing Date 2017-04-27
Publication Date 2017-11-02
Owner FRIDAY HARBOR LLC (USA)
Inventor Palmer, Douglas A.

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing an intelligence aggregation system. One of the methods includes receiving, by an agent, one or more goal criteria. A search to identify one or more other agents in the system is performed. Connections with the one or more other agents are established, with each connection having an initial weight. Data outputs generated by each connected agent are received to iteratively update a model using the received data outputs and associated weights. If the current model generated from current weights for the connections of the one or more connected agents satisfies the one or more goal criteria, the output of the current model is published to a search engine.

IPC Classes  ?

  • G06N 99/00 - Subject matter not provided for in other groups of this subclass

3.

ESTIMATING PITCH OF HARMONIC SIGNALS

      
Application Number US2017026432
Publication Number 2017/177056
Status In Force
Filing Date 2017-04-06
Publication Date 2017-10-12
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Bradley, David C.
  • Semko, Jeremy

Abstract

A time-varying pitch of a signal may be estimated by processing a sequence of frames of the speech signal. An estimated fractional chirp rate may be computed for each frame of the sequence of frames, and the estimated fractional chirp rates may be used to compute a pitch template for the sequence, where the pitch template indicates the time-varying pitch of the signal subject to a scale factor. A first pitch estimate for each frame of the sequence of frames may be computed by computing a scale factor and multiplying the pitch template by the scale factor. A second pitch estimate may be computed from the first pitch estimate by identifying peaks in the frequency representations using the first pitch estimates and fitting a parametric function to the peaks.

IPC Classes  ?

  • G10L 25/90 - Pitch determination of speech signals

4.

Memory-attached computing resource in network on a chip architecture to perform calculations on data stored on memory external to the chip

      
Application Number 15043000
Grant Number 09959066
Status In Force
Filing Date 2016-02-12
First Publication Date 2017-08-17
Grant Date 2018-05-01
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Palmer, Douglas A.
  • Coffin, Jerome V.
  • Clevenger, William Christensen

Abstract

A computing system includes a plurality of computing resources that communicate with each other using network on a chip architecture. One of the plurality of computing resources is attached to memory external to the computing system through an external memory interface. The memory-attached computing resource is configured to read data from the memory and modify the read data prior to either writing the modified data back to the memory, or transmitting the modified data to one or more other of the computing resources, or both.

IPC Classes  ?

  • G06F 13/00 - Interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units
  • G06F 3/06 - Digital input from, or digital output to, record carriers
  • H04L 12/861 - Packet buffering or queuing arrangements; Queue scheduling

5.

Flow control through packet router

      
Application Number 15265211
Grant Number 09977745
Status In Force
Filing Date 2016-09-14
First Publication Date 2017-07-06
Grant Date 2018-05-22
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Florea, Michael
  • Coffin, Jerome Vincent

Abstract

A router requests a reservation for an egress port prior to dequeuing data from an ingress port data queue. A request is also made for the allocation of a buffer. After the reservation is received and a buffer is allocated, the data is copied the ingress port data queue to the buffer, and an identifier of the buffer is enqueued to an identifier queue of the egress port. After the identifier is dequeued, the data is copied from the buffer to an egress data queue of the egress port, and the buffer is released for reallocation. The buffer can be released prior to completion of the data being copied from the buffer. The queues associated with the egress port determine whether their depths equal or exceed a threshold associated with the respective egress queues. If one or more of the depths does equal or exceed the associated threshold, the granting of reservations for the egress port are postponed. The thresholds are each set to less than full capacity, so as to indicate that the respective queue is congested, rather than to indicate that they have no space available.

IPC Classes  ?

  • H04L 12/50 - Circuit switching systems, i.e. systems in which the path is physically permanent during the communication
  • G06F 12/10 - Address translation
  • H04L 12/863 - Queue scheduling, e.g. Round Robin
  • H04L 12/911 - Network admission control and resource allocation, e.g. bandwidth allocation or in-call renegotiation
  • H04L 12/913 - Reservation actions involving intermediate nodes, e.g. resource reservation protocol [RSVP]
  • H04L 12/937 - Switch control, e.g. arbitration
  • H04L 12/935 - Switch interfaces, e.g. port details
  • H04L 12/931 - Switch fabric architecture
  • H04L 12/721 - Routing procedures, e.g. shortest path routing, source routing, link state routing or distance vector routing
  • H04L 12/741 - Header address processing for routing, e.g. table lookup
  • H04L 29/06 - Communication control; Communication processing characterised by a protocol

6.

Harmonic feature processing for reducing noise

      
Application Number 15401608
Grant Number 09704506
Status In Force
Filing Date 2017-01-09
First Publication Date 2017-05-25
Grant Date 2017-07-11
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Bradley, David C.
  • Morin, Yao Huang

Abstract

Devices, systems and methods are disclosed for reducing noise in input data by performing a hysteresis operation followed by a lateral excitation smoothing operation. For example, an audio signal may be represented as a sequence of feature vectors. A row of the sequence of feature vectors may, for example, be associated with the same harmonic of the audio signal at different points in time. To determine portions of the row that correspond to the harmonic being present, the system may compare an amplitude to a low threshold and a high threshold and select a series of data points that are above the low threshold and include at least one data point above the high threshold. The system may iteratively perform a spreading technique, spreading a center value of a center data point in a kernel to neighboring data points in the kernel, to further reduce noise.

IPC Classes  ?

  • G10L 21/00 - Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
  • G10L 21/038 - Speech enhancement, e.g. noise reduction or echo cancellation using band spreading techniques
  • G10L 21/02 - Speech enhancement, e.g. noise reduction or echo cancellation
  • G10L 25/18 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
  • G10L 21/0388 - Speech enhancement, e.g. noise reduction or echo cancellation using band spreading techniques - Details of processing therefor
  • G10L 21/0264 - Noise filtering characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques
  • G10L 21/0232 - Processing in the frequency domain
  • G10L 17/02 - Preprocessing operations, e.g. segment selection; Pattern representation or modelling, e.g. based on linear discriminant analysis [LDA] or principal components; Feature selection or extraction
  • G10L 15/02 - Feature extraction for speech recognition; Selection of recognition unit

7.

I/O data interface for packet processors

      
Application Number 14500694
Grant Number 09614785
Status In Force
Filing Date 2014-09-29
First Publication Date 2017-04-04
Grant Date 2017-04-04
Owner FRIDAY HARBOR LLC (USA)
Inventor Palmer, Douglas A.

Abstract

Systems and methods to process packets of information use an on-chip information processing system configured to receive, resolve, convert, and/or transmit packets of different packet-types in accordance with different protocols. A first packet-type may use a protocol for wired local-area-networking (LAN) technologies, such as Ethernet. A second packet-type may use a proprietary protocol. The proprietary protocol may be used to exchange information with one or more packet processing engines, such as neural processing engines.

IPC Classes  ?

  • H04L 12/933 - Switch core, e.g. crossbar, shared memory or shared medium
  • H04L 12/935 - Switch interfaces, e.g. port details
  • H04L 12/931 - Switch fabric architecture
  • H04L 29/12 - Arrangements, apparatus, circuits or systems, not covered by a single one of groups characterised by the data terminal

8.

Performing read operations in network on a chip architecture

      
Application Number 14832654
Grant Number 09686191
Status In Force
Filing Date 2015-08-21
First Publication Date 2017-02-23
Grant Date 2017-06-20
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • White, Andy
  • Meyer, Doug
  • Coffin, Jerry

Abstract

Systems and methods to be used by a processing element from among multiple computing resources of a computing system, where communication between the computing resources is carried out based on network on a chip architecture, to send first data from memory registers of the processing element and second data from memory of the computing system to a destination processing element from among the multiple computing resources, by sending the first data to a memory controller of the memory along with a single appended-read command.

IPC Classes  ?

  • G06F 12/00 - Accessing, addressing or allocating within memory systems or architectures
  • H04L 12/721 - Routing procedures, e.g. shortest path routing, source routing, link state routing or distance vector routing
  • G06F 12/109 - Address translation for multiple virtual address spaces, e.g. segmentation
  • H04L 12/935 - Switch interfaces, e.g. port details
  • H04L 12/861 - Packet buffering or queuing arrangements; Queue scheduling

9.

Reducing octave errors during pitch determination for noisy audio signals

      
Application Number 13945731
Grant Number 09530434
Status In Force
Filing Date 2013-07-18
First Publication Date 2016-12-27
Grant Date 2016-12-27
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Mascaro, Massimo
  • Bradley, David C.

Abstract

Octave errors may be reduced during pitch determination for noisy audio signals. Pitch may be tracked over time by determining amplitudes at harmonics for individual time windows of an input signal. Octave errors may be reduced in individual time windows by fitting amplitudes of corresponding harmonics across successive time windows to identify spurious harmonics caused by octave error. A given harmonic may be identified as either being associated with the same pitch as adjacent harmonics in the given time window or being spurious based on parameters of the fitting function.

IPC Classes  ?

  • G10L 25/90 - Pitch determination of speech signals

10.

Voice enhancement and/or speech features extraction on noisy audio signals using successively refined transforms

      
Application Number 13944750
Grant Number 09484044
Status In Force
Filing Date 2013-07-17
First Publication Date 2016-11-01
Grant Date 2016-11-01
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Mascaro, Massimo
  • Bradley, David C.

Abstract

Voice enhancement and/or speech features extraction may be performed on noisy audio signals using successively refined transforms. Downsampled versions of an input signal may be obtained, which include a first downsampled signal with a lower sampling rate than a second downsampled signal. Successive transforms may be performed on the input signal to obtain a corresponding sound model of the input signal. The successive transforms performed may include: (1) performing a first transform on the first downsampled signal to yield a first pitch estimate; (2) performing a second transform on the second downsampled signal to yield a second pitch estimate and a first harmonics estimate based on the first pitch estimate; and (3) performing a third transform on the input signal to yield a third pitch estimate and a second harmonics estimate based on the second pitch estimate and the first harmonics estimate.

IPC Classes  ?

11.

Low-latency network interface

      
Application Number 15068407
Grant Number 09565124
Status In Force
Filing Date 2016-03-11
First Publication Date 2016-09-15
Grant Date 2017-02-07
Owner FRIDAY HARBOR LLC (USA)
Inventor Kerr, Richard

Abstract

Methods, systems, and apparatus for a low-latency network interface. One of the methods includes receiving a signal having encoded data. A bit stream is generated from the received signal. Bits of the bit stream are shifted into a shift register until a feedback signal generated by a synchronization decoder is received. After the feedback signal is received, output of the shift register is descrambled to generate descrambled data. The descrambled data is stored in a first parallel register when the synchronization decoder determines that the data in the shift register is aligned to a word boundary. If the data in the first parallel register is properly aligned, the output is stored in a second parallel register.

IPC Classes  ?

  • H04L 7/06 - Speed or phase control by synchronisation signals the synchronisation signals differing from the information signals in amplitude, polarity, or frequency
  • H04L 25/40 - Transmitting circuits; Receiving circuits
  • H04L 12/875 - Delay-aware scheduling

12.

Clustering of audio files using graphs

      
Application Number 14718823
Grant Number 09691391
Status In Force
Filing Date 2015-05-21
First Publication Date 2016-08-11
Grant Date 2017-06-27
Owner FRIDAY HARBOR LLC (USA)
Inventor Gateau, Rodney

Abstract

Systems and methods to perform speaker clustering determine which audio segments appear to include sound generated by the same speaker. Speaker clustering is based on creating a graph in which a node represents an audio segment and an edge between two nodes represents a relationship and/or correspondence that reflects a probability, likelihood, or other indication that the two nodes represent audio segments of the same speaker. This graph is analyzed to detect individual communities of nodes that associate to an individual speaker.

IPC Classes  ?

  • G10L 17/00 - Speaker identification or verification
  • H04R 3/00 - Circuits for transducers
  • G10L 17/06 - Decision making techniques; Pattern matching strategies

13.

Harmonic feature processing for reducing noise

      
Application Number 15016801
Grant Number 09576589
Status In Force
Filing Date 2016-02-05
First Publication Date 2016-08-11
Grant Date 2017-02-21
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Bradley, David C
  • Morin, Yao Huang

Abstract

Devices, systems and methods are disclosed for reducing noise in input data by performing a hysteresis operation followed by a lateral excitation smoothing operation. For example, an audio signal may be represented as a sequence of feature vectors. A row of the sequence of feature vectors may, for example, be associated with the same harmonic of the audio signal at different points in time. To determine portions of the row that correspond to the harmonic being present, the system may compare an amplitude to a low threshold and a high threshold and select a series of data points that are above the low threshold and include at least one data point above the high threshold. The system may iteratively perform a spreading technique, spreading a center value of a center data point in a kernel to neighboring data points in the kernel, to further reduce noise.

IPC Classes  ?

  • G10L 15/00 - Speech recognition
  • G10L 21/0232 - Processing in the frequency domain
  • G10L 21/0208 - Noise filtering
  • G10L 21/0264 - Noise filtering characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques
  • G10L 25/90 - Pitch determination of speech signals

14.

Estimation of noise characteristics

      
Application Number 14860999
Grant Number 09812148
Status In Force
Filing Date 2015-09-22
First Publication Date 2016-08-11
Grant Date 2017-11-07
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Bradley, David C
  • Morin, Yao

Abstract

Devices, systems and methods are disclosed for estimating characteristics of noise included in one-dimensional data. For example, a number of data points associated with noise below each of a plurality of thresholds may be determined to calculate a cumulative distribution function. A probability density function may be derived from the cumulative distribution function. A variance may be calculated from the cumulative distribution function and/or the probability density function. The noise may be modeled using the variance and other characteristics determined from the cumulative distribution function and/or the probability density function.

IPC Classes  ?

  • H04B 15/00 - Suppression or limitation of noise or interference
  • G10L 21/0216 - Noise filtering characterised by the method used for estimating noise
  • G10L 21/0264 - Noise filtering characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques

15.

Estimating pitch using peak-to-peak distances

      
Application Number 14969038
Grant Number 09842611
Status In Force
Filing Date 2015-12-15
First Publication Date 2016-08-11
Grant Date 2017-12-12
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Bradley, David C.
  • Morin, Yao Huang
  • Marongelli, Ellisha

Abstract

An estimate of a pitch of a signal may be computed by using peak-to-peak distances in a frequency representation of the signal. A frequency representation of the signal may be computed, peaks in the frequency representation may be identified, for example, by identifying peaks larger than a threshold value. Peak-to-peak distances may be determined using the locations in frequency of the peaks. The pitch of the signal may be estimated by, for example, estimating cumulative distribution function of the peak-to-peak distances or computing a histogram of the peak-to-peak distances.

IPC Classes  ?

  • G10L 25/90 - Pitch determination of speech signals
  • G10L 25/18 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band

16.

Memory controller for a network on a chip device

      
Application Number 14608515
Grant Number 09552327
Status In Force
Filing Date 2015-01-29
First Publication Date 2016-08-04
Grant Date 2017-01-24
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Palmer, Douglas A.
  • Zuniga, Ramon

Abstract

Systems and methods may be provided to support memory access by packet communication and/or direct memory access. In one aspect, a memory controller may be provided for a processing device containing a plurality of computing resources. The memory controller may comprise a first interface to be coupled to a router. The first interface may be configured to transmit and receive packets. Each packet may comprise a header that may contain a routable address and a packet opcode specifying an operation to be performed in accordance with a network protocol. The memory controller may further comprise a memory bus port coupled to a plurality of memory slots that are configured to receive memory banks to form a memory and a controller core coupled to the first interface. The controller core may be configured to decode a packet received at the first interface and perform an operation specified in the received packet.

IPC Classes  ?

  • G06F 13/00 - Interconnection of, or transfer of information or other signals between, memories, input/output devices or central processing units
  • G06F 15/78 - Architectures of general purpose stored program computers comprising a single central processing unit
  • G06F 13/16 - Handling requests for interconnection or transfer for access to memory bus
  • H04L 12/717 - Centralised routing
  • H04L 12/715 - Hierarchical routing, e.g. clustered networks or inter-domain routing

17.

Topology discovery in a computing system

      
Application Number 14608670
Grant Number 09634901
Status In Force
Filing Date 2015-01-29
First Publication Date 2016-08-04
Grant Date 2017-04-25
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Palmer, Douglas A.
  • Meyer, Doug B.
  • Coffin, Jerome V.

Abstract

A computer network may comprise a plurality of computing devices. In one example, a method may be provided for discovering topology of the computer network. The method may comprise sending, by a host computing device of the computing network, a neighbor discovery packet to each network interface of the host that has a connection, receiving a reply packet responding to the neighbor discovery packet, building a neighbor map for all neighbor computing devices to the host, sending a connection discovery packet to each network interface of the host that has a connection, receiving reply packets responding to the connection discovery packet, and building a connection map for connections among computing devices based on the information in the reply packets.

IPC Classes  ?

18.

Systems and methods for estimating pitch in audio signals based on symmetry characteristics independent of harmonic amplitudes

      
Application Number 14502844
Grant Number 09396740
Status In Force
Filing Date 2014-09-30
First Publication Date 2016-07-19
Grant Date 2016-07-19
Owner FRIDAY HARBOR LLC (USA)
Inventor Bradley, David C.

Abstract

Pitch in audio signals may be estimated based on symmetry characteristics independent of harmonic amplitudes. A magnitude spectrum of an audio signal may be provided. The magnitude spectrum may be partitioned by dividing a frequency axis into equal-sized cells. Individual cells may be centered on corresponding harmonic frequencies of a hypothesized pitch. The magnitude spectrum contained in individual cells may be normalized to have equal mean magnitudes and equal standard deviations. A likelihood that the hypothesized pitch is an actual pitch of the audio signal may be determined based on symmetries of magnitude spectra contained in individual cells.

IPC Classes  ?

  • G10L 11/04 - Pitch determination of speech signals
  • G10L 25/90 - Pitch determination of speech signals
  • G10L 25/12 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being prediction coefficients
  • G10L 25/15 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being formant information
  • G10L 21/0264 - Noise filtering characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques
  • G10L 25/00 - Speech or voice analysis techniques not restricted to a single one of groups

19.

Estimating pitch using symmetry characteristics

      
Application Number 14969022
Grant Number 09548067
Status In Force
Filing Date 2015-12-15
First Publication Date 2016-04-07
Grant Date 2017-01-17
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Bradley, David C.
  • Morin, Yao Huang
  • O'Connor, Sean

Abstract

An estimate of a pitch of a signal may be computed by using correlations of frequency portions of a frequency representation of the signal. An initial pitch estimate may be obtained and frequency portions of the frequency representation may be identified using multiples of the initial pitch estimate. Correlations of the frequency portions may be computed, and a score for the initial pitch estimate may be determined using the correlations. A second pitch estimate may be determined using the first score, and the process may be repeated.

IPC Classes  ?

  • G10L 11/04 - Pitch determination of speech signals
  • G10L 25/90 - Pitch determination of speech signals
  • G10L 25/06 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being correlation coefficients
  • G10L 25/00 - Speech or voice analysis techniques not restricted to a single one of groups
  • G10L 25/15 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being formant information
  • G10L 21/0264 - Noise filtering characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques

20.

Systems and methods for segmenting and/or classifying an audio signal from transformed audio information

      
Application Number 14481918
Grant Number 09601119
Status In Force
Filing Date 2014-09-10
First Publication Date 2014-12-25
Grant Date 2017-03-21
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Bradley, David C.
  • Hilton, Robert N.
  • Goldin, Daniel S.
  • Fisher, Nicholas K.
  • Roos, Derrick R.
  • Wiewiora, Eric

Abstract

A system and method may be provided to segment and/or classify an audio signal from transformed audio information. Transformed audio information representing a sound may be obtained. The transformed audio information may specify magnitude of a coefficient related to energy amplitude as a function of frequency for the audio signal and time. Features associated with the audio signal may be obtained from the transformed audio information. Individual ones of the features may be associated with a feature score relative to a predetermined speaker model. An aggregate score may be obtained based on the feature scores according to a weighting scheme. The weighting scheme may be associated with a noise and/or SNR estimation. The aggregate score may be used for segmentation to identify portions of the audio signal containing speech of one or more different speakers. For classification, the aggregate score may be used to determine a likely speaker model to identify a source of the sound in the audio signal.

IPC Classes  ?

  • H04R 29/00 - Monitoring arrangements; Testing arrangements
  • G10L 17/02 - Preprocessing operations, e.g. segment selection; Pattern representation or modelling, e.g. based on linear discriminant analysis [LDA] or principal components; Feature selection or extraction
  • H04R 3/00 - Circuits for transducers
  • G10L 25/51 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination
  • G10L 25/84 - Detection of presence or absence of voice signals for discriminating voice from noise

21.

System and method for processing sound signals implementing a spectral motion transform

      
Application Number 14320556
Grant Number 09620130
Status In Force
Filing Date 2014-06-30
First Publication Date 2014-12-25
Grant Date 2017-04-11
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Bradley, David C.
  • Goldin, Daniel S.
  • Hilton, Robert N.
  • Fisher, Nicholas K.
  • Gateau, Rodney
  • Roos, Derrick R.
  • Wiewiora, Eric

Abstract

A system and method are provided for processing sound signals. The processing may include identifying individual harmonic sounds represented in sound signals, determining sound parameters of harmonic sounds, classifying harmonic sounds according to source, and/or other processing. The processing may include transforming the sound signals (or portions thereof) into a space which expresses a transform coefficient as a function of frequency and chirp rate. This may facilitate leveraging of the fact that the individual harmonics of a single harmonic sound may have a common pitch velocity (which is related to the chirp rate) across all of its harmonics in order to distinguish an the harmonic sound from other sounds (harmonic and/or non-harmonic) and/or noise.

IPC Classes  ?

  • H03G 5/00 - Tone control or bandwidth control in amplifiers
  • G10L 19/00 - Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
  • G10L 25/93 - Discriminating between voiced and unvoiced parts of speech signals
  • G10L 21/0208 - Noise filtering
  • H04R 29/00 - Monitoring arrangements; Testing arrangements
  • G10L 13/00 - Speech synthesis; Text to speech systems
  • G10L 21/00 - Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
  • H03G 5/16 - Automatic control
  • G10L 25/45 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of analysis window
  • G10L 25/90 - Pitch determination of speech signals
  • G10L 21/0232 - Processing in the frequency domain

22.

System and method for tracking sound pitch across an audio signal using harmonic envelope

      
Application Number 14089729
Grant Number 09473866
Status In Force
Filing Date 2013-11-25
First Publication Date 2014-03-27
Grant Date 2016-10-18
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Bradley, David C.
  • Gateau, Rodney
  • Goldin, Daniel S.
  • Hilton, Robert N.
  • Fisher, Nicholas K.

Abstract

A system and method may be configured to analyze audio information derived from an audio signal. The system and method may track sound pitch across the audio signal. The tracking of pitch across the audio signal may take into account change in pitch by determining at individual time sample windows in the signal duration an estimated pitch and a representation of harmonic envelope at the estimated pitch. The estimated pitch and the representation of harmonic envelope may then be implemented to determine an estimated pitch for another time sample window in the signal duration with an enhanced accuracy and/or precision.

IPC Classes  ?

  • G06F 15/00 - Digital computers in general; Data processing equipment in general
  • G10L 25/00 - Speech or voice analysis techniques not restricted to a single one of groups
  • G10L 19/00 - Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
  • G10L 21/00 - Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
  • G10L 25/90 - Pitch determination of speech signals
  • G10L 19/12 - Determination or coding of the excitation function; Determination or coding of the long-term prediction parameters the excitation function being a code excitation, e.g. in code excited linear prediction [CELP] vocoders
  • G10L 21/04 - Time compression or expansion
  • H04R 29/00 - Monitoring arrangements; Testing arrangements

23.

System and method of processing a sound signal including transforming the sound signal into a frequency-chirp domain

      
Application Number 14040418
Grant Number 09485597
Status In Force
Filing Date 2013-09-27
First Publication Date 2014-02-06
Grant Date 2016-11-01
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Bradley, David C.
  • Goldin, Daniel S.
  • Hilton, Robert N.
  • Fisher, Nicholas K.
  • Gateau, Rodney
  • Roos, Derrick R.
  • Wiewiora, Eric

Abstract

A system and method may be configured to process an audio signal. The system and method may track pitch, chirp rate, and/or harmonic envelope across the audio signal, may reconstruct sound represented in the audio signal, and/or may segment or classify the audio signal. A transform may be performed on the audio signal to place the audio signal in a frequency chirp domain that enhances the sound parameter tracking, reconstruction, and/or classification.

IPC Classes  ?

  • G10L 15/00 - Speech recognition
  • G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
  • G10L 13/00 - Speech synthesis; Text to speech systems
  • G10L 13/08 - Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination
  • G10L 21/00 - Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
  • G10L 25/00 - Speech or voice analysis techniques not restricted to a single one of groups
  • G10L 19/02 - Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders
  • G10L 25/90 - Pitch determination of speech signals
  • G10L 21/02 - Speech enhancement, e.g. noise reduction or echo cancellation
  • G10L 15/20 - Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise or of stress induced speech
  • G10L 17/00 - Speaker identification or verification
  • G06F 17/27 - Automatic analysis, e.g. parsing, orthograph correction
  • H04R 29/00 - Monitoring arrangements; Testing arrangements
  • G10L 25/03 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters
  • G10L 25/27 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the analysis technique
  • H04R 3/02 - Circuits for transducers for preventing acoustic reaction
  • H04B 15/00 - Suppression or limitation of noise or interference
  • G10L 15/02 - Feature extraction for speech recognition; Selection of recognition unit
  • G10L 17/02 - Preprocessing operations, e.g. segment selection; Pattern representation or modelling, e.g. based on linear discriminant analysis [LDA] or principal components; Feature selection or extraction
  • G10L 21/0208 - Noise filtering
  • G10L 25/51 - Speech or voice analysis techniques not restricted to a single one of groups specially adapted for particular use for comparison or discrimination
  • G10L 25/93 - Discriminating between voiced and unvoiced parts of speech signals
  • G10L 25/15 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being formant information
  • G10L 21/0232 - Processing in the frequency domain

24.

Speaker segmentation in noisy conversational speech

      
Application Number 13097620
Grant Number 08543402
Status In Force
Filing Date 2011-04-29
First Publication Date 2013-09-24
Grant Date 2013-09-24
Owner FRIDAY HARBOR LLC (USA)
Inventor Ma, Jiyong

Abstract

System and methods for robust multiple speaker segmentation in noisy conversational speech are presented. Robust voice activity detection is applied to detect temporal speech events. In order to get robust speech features and detect speech events in a noisy environment, a noise reduction algorithm is applied, using noise tracking. After noise reduction and voice activity detection, the incoming audio/speech is initially labeled as speech segments or silence segments. With no prior knowledge of the number of speakers, the system identifies one reliable speech segment near the beginning of the conversational speech and extracts speech features with a short latency, then learns a statistical model from the selected speech segment. This initial statistical model is used to identify the succeeding speech segments in a conversation. The statistical model is also continuously adapted and expanded with newly identified speech segments that match well to the model. The speech segments with low likelihoods are labeled with a second speaker ID, and a statistical model is learned from them. At the same time, these two trained speaker models are also updated/adapted once a reliable speech segment is identified. If a speech segment does not match well to the two speaker models, the speech segment is temporarily labeled as an outlier or as originating from a third speaker. This procedure is then applied recursively as needed when there are more than two speakers in a conversation.

IPC Classes  ?

  • G10L 15/06 - Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice

25.

System and method of processing a sound signal including transforming the sound signal into a frequency-chirp domain

      
Application Number 13205535
Grant Number 08548803
Status In Force
Filing Date 2011-08-08
First Publication Date 2013-02-14
Grant Date 2013-10-01
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Bradley, David C.
  • Goldin, Daniel S.
  • Hilton, Robert N.
  • Fisher, Nicholas K.
  • Gateau, Rodney
  • Roos, Derrick R.
  • Wiewiora, Eric

Abstract

A system and method may be configured to process an audio signal. The system and method may track pitch, chirp rate, and/or harmonic envelope across the audio signal, may reconstruct sound represented in the audio signal, and/or may segment or classify the audio signal. A transform may be performed on the audio signal to place the audio signal in a frequency chirp domain that enhances the sound parameter tracking, reconstruction, and/or classification.

IPC Classes  ?

  • G10L 11/06 - Discriminating between voiced and unvoiced parts of speech signals (G10L 11/04 takes precedence);;
  • G10L 11/00 - Determination or detection of speech or audio characteristics not restricted to a single one of groups ; G10L 15/00-G10L 21/00
  • G10L 11/04 - Pitch determination of speech signals
  • G10L 19/00 - Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
  • G10L 19/14 - Details not provided for in groups ; G10L 19/06-G10L 19/12, e.g. gain coding, post filtering design or vocoder structure

26.

System and method for tracking sound pitch across an audio signal using harmonic envelope

      
Application Number 13205521
Grant Number 08620646
Status In Force
Filing Date 2011-08-08
First Publication Date 2013-02-14
Grant Date 2013-12-31
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Bradley, David C.
  • Gateau, Rodney
  • Goldin, Daniel S.
  • Hilton, Robert N.
  • Fisher, Nicholas K.

Abstract

A system and method may be configured to analyze audio information derived from an audio signal. The system and method may track sound pitch across the audio signal. The tracking of pitch across the audio signal may take into account change in pitch by determining at individual time sample windows in the signal duration an estimated pitch and a representation of harmonic envelope at the estimated pitch. The estimated pitch and the representation of harmonic envelope may then be implemented to determine an estimated pitch for another time sample window in the signal duration with an enhanced accuracy and/or precision.

IPC Classes  ?

  • G06F 15/00 - Digital computers in general; Data processing equipment in general
  • G10L 21/00 - Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
  • G10L 19/00 - Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
  • G10L 25/90 - Pitch determination of speech signals
  • G10L 19/12 - Determination or coding of the excitation function; Determination or coding of the long-term prediction parameters the excitation function being a code excitation, e.g. in code excited linear prediction [CELP] vocoders
  • G10L 15/20 - Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise or of stress induced speech
  • G10L 21/02 - Speech enhancement, e.g. noise reduction or echo cancellation
  • G10L 17/00 - Speaker identification or verification
  • G10L 13/00 - Speech synthesis; Text to speech systems
  • G10L 19/02 - Speech or audio signal analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders

27.

Systems and methods using neural networks to reduce noise in audio signals

      
Application Number 12883313
Grant Number 08543526
Status In Force
Filing Date 2010-09-16
First Publication Date 2011-11-03
Grant Date 2013-09-24
Owner FRIDAY HARBOR LLC (USA)
Inventor Moore, Douglas A.

Abstract

Systems, methods, and computer program products are provided to provide noise reduction for an input signal using a neural network. A feed-forward set of neuron groups is provided to enhance neuron activity within a particular frequency band based on prior reception of activity within that frequency band, and also to attenuate surrounding frequency bands. A surround-inhibition set of neuron groups further attenuates activity surrounding the stimulated frequency band.

IPC Classes  ?

  • G06F 15/00 - Digital computers in general; Data processing equipment in general
  • G06F 17/00 - Digital computing or data processing equipment or methods, specially adapted for specific functions
  • G06N 3/10 - Interfaces, programming languages or software development kits, e.g. for simulating neural networks

28.

Neural network for clustering input data based on a Gaussian Mixture Model

      
Application Number 12850352
Grant Number 08521671
Status In Force
Filing Date 2010-08-04
First Publication Date 2011-11-03
Grant Date 2013-08-27
Owner FRIDAY HARBOR LLC (USA)
Inventor Moore, Douglas A.

Abstract

Disclosed are systems, apparatuses, and methods for clustering data. Such a method includes providing input data to each of a plurality of cluster microcircuits of a neural network, wherein each cluster microcircuit includes a mean neural group and a variance neural group. The method also includes determining a response of each cluster microcircuit with respect to the input data. The method further includes modulating the mean neural group and the variance neural group of each cluster microcircuit responsive to a value system.

IPC Classes  ?

  • G06E 1/00 - Devices for processing exclusively digital data
  • G06E 3/00 - Devices not provided for in group , e.g. for processing analogue or hybrid data
  • G06F 15/18 - in which a program is changed according to experience gained by the computer itself during a complete run; Learning machines (adaptive control systems G05B 13/00;artificial intelligence G06N)
  • G06G 7/00 - Devices in which the computing operation is performed by varying electric or magnetic quantities

29.

Neural segmentation of an input signal and applications using simulated neurons, and a phase modulator

      
Application Number 12621243
Grant Number 08473436
Status In Force
Filing Date 2009-11-18
First Publication Date 2011-05-19
Grant Date 2013-06-25
Owner FRIDAY HARBOR LLC (USA)
Inventor
  • Moore, Douglas A.
  • Tsukida, Kristi H.
  • Ang, Paulo B.

Abstract

Disclosed are systems, methods, and computer-program products for segmenting content of an input signal and applications thereof. In an embodiment, the system includes simulated neurons, a phase modulator, and an entity-identifier module. Each simulated neuron is connected to one or more other simulated neurons and is associated with an activity and a phase. The activity and the phase of each simulated neuron is set based on the activity and the phase of the one or more other simulated neurons connected to each simulated neuron. The phase modulator includes individual modulators, each configured to modulate the activity and the phase of each of the plurality of simulated neurons based on a modulation function. The entity-identifier module is configured to identify one or more distinct entities (e.g., objects, sound sources, etc.) included in the input signal based on the one or more distinct collections of simulated neurons that have substantially distinct phases.

IPC Classes  ?

  • G06E 1/00 - Devices for processing exclusively digital data

30.

Constant memory implementation of a phase-model neural network

      
Application Number 12836839
Grant Number 08504499
Status In Force
Filing Date 2010-07-15
First Publication Date 2011-01-20
Grant Date 2013-08-06
Owner FRIDAY HARBOR LLC (USA)
Inventor Lewi, Jeremy M.

Abstract

Disclosed are systems, apparatuses, and methods for implementing a phase-model neural network using a fixed amount of memory. Such a phase-model neural network includes a plurality of neurons, wherein each neuron is associated with two parameters—an activity and a phase. Example methods include (i) generating a sequence of variables associated with a probability distribution of phases and (ii) sequentially sampling the probability distribution of phases using a fixed amount of memory, regardless of a number of phases used in the phase-model neural network.

IPC Classes  ?

  • G06E 1/00 - Devices for processing exclusively digital data
  • G06E 3/00 - Devices not provided for in group , e.g. for processing analogue or hybrid data
  • G06F 15/18 - in which a program is changed according to experience gained by the computer itself during a complete run; Learning machines (adaptive control systems G05B 13/00;artificial intelligence G06N)
  • G06G 7/00 - Devices in which the computing operation is performed by varying electric or magnetic quantities
  • G06N 3/02 - Neural networks