Abstract: A method and apparatus for canceling an echo in audio communication is disclosed. The method comprises receiving an audio signal from a network and subsequently detecting a mixture audio signal comprising a target audio signal and an echo audio signal, the echo signal corresponding to the received audio signal. The method then comprises estimating the target audio signal by determining magnitude spectrograms for the mixture and received audio signals respectively, estimating a magnitude spectrogram of the target audio signal dependent on those of the mixture and received audio signal, and generating an output audio signal that estimates the target audio signal, the output audio signal being dependent on the estimated magnitude spectrogram.

Abstract: A system and method are described for selecting a target sound object from a sound mixture. In embodiments, a sound mixture comprises a plurality of sound objects superimposed in time. A user can select one of these sound objects by providing reference audio data corresponding to a reference sound object. The system analyzes the audio data and the reference audio data to identify a portion of the audio data corresponding to a target sound object in the mixture that is most similar to the reference sound object. The analysis may include decomposing the reference audio data into a plurality of reference components and the sound mixture into a plurality of components guided by the reference components. The target sound object can be re-synthesized from the target components.

Abstract: A sound mixture may be received that includes a plurality of sources. A model may be received that includes a dictionary of spectral basis vectors for the plurality of sources. A weight may be estimated for each of the plurality of sources in the sound mixture based on the model. In some examples, such weight estimation may be performed using a source separation technique without actually separating the sources.

Abstract: Clustering and synchronizing content may include extracting audio features for each of a plurality of files that include audio content. The plurality of files may be clustered into one or more clusters. Clustering may include clustering based on a histogram that may be generated for each file pair of the plurality of files. Within each of the clusters, the files of the cluster may be time aligned.

Abstract: Methods and apparatus for relative pitch tracking of multiple arbitrary sounds. A probabilistic method for pitch tracking may be implemented as or in a pitch tracking module. A constant-Q transform of an input signal may be decomposed to estimate one or more kernel distributions and one or more impulse distributions. Each kernel distribution represents a spectrum of a particular source, and each impulse distribution represents a relative pitch track for a particular source. The decomposition of the constant-Q transform may be performed according to shift-invariant probabilistic latent component analysis, and may include applying an expectation maximization algorithm to estimate the kernel distributions and the impulse distributions. When decomposing, a prior, e.g. a sliding-Gaussian Dirichlet prior or an entropic prior, and/or a temporal continuity constraint may be imposed on each impulse distribution.