Knuedge, Inc.

The Intellisis Corporation

Friday Harbor LLC

David C Bradley

Robert Nicholas Hilton

Daniel S Goldin

Nicholas K Fisher

Rodney Gateau

Yao H Morin

Derrick Raymond Roos

Eric Wiewiora

Massimo Mascaro

Sean Michael O'Connor

Ellisha Natalie Marongelli

Jeremy Semko

Janis Intoy

Nick Hilton

Robert N Hilton

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.

Estimating pitch of harmonic signals

An input signal may be classified by computing correlations between feature vectors of the input signal and feature vectors of reference signals, wherein the reference signals correspond to a class. The feature vectors of the input signal and/or the reference signals may be segmented to identify portions of the signals before performing the correlations. Multiple correlations of the segments may be combined to produce a segment score corresponding to a segment. The signal may then be classified using multiple segment scores, for example by comparing a combination of the segment scores to a threshold.

Classifying signals using correlations of segments

An estimate of a fractional chirp rate of a signal may be computed by using multiple frequency representations of the signal. A first frequency representation may be computed using a first fractional chirp rate and a first score may be computed using the first frequency representation that indicates a match between the first fractional chirp rate and a fractional chirp rate of the signal. A second frequency representation may be computed using a second fractional chirp rate and a second score may be computed using the second frequency representation that indicates a match between the second fractional chirp rate and the fractional chirp rate of the signal. The fractional chirp rate of the signal may be estimated using the first score and the second score, for example, by selecting a fractional chirp rate corresponding to a highest score.

Estimating fractional chirp rate with multiple frequency representations

Features that may be computed from a harmonic signal include a fractional chirp rate, a pitch, and amplitudes of the harmonics. A fractional chirp rate may be estimated, for example, by computing scores corresponding to different fractional chirp rates and selecting a highest score. A first pitch may be computed from a frequency representation that is computed using the estimated fractional chirp rate, for example, by using peak-to-peak distances in the frequency distribution. A second pitch may be computed using the first pitch, and a frequency representation of the signal, for example, by using correlations of portions of the frequency representation. Amplitudes of harmonics of the signal may be determined using the estimated fractional chirp rate and second pitch. Any of the estimated fractional chirp rate, second pitch, and harmonic amplitudes may be used for further processing, such as speech recognition, speaker verification, speaker identification, or signal reconstruction.

Determining features of harmonic signals

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.

Estimating pitch using peak-to-peak distances

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.

Estimation of noise characteristics

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.

Harmonic feature processing for reducing noise

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.

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

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.

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

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.

Estimating pitch using symmetry characteristics

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.

Reducing octave errors during pitch determination for noisy audio signals

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.

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

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.

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La Jolla, CA, United States of America

David C Bradley