Abstract: Variable sound decomposition masking techniques are described. In one or more implementations, a mask is generated that incorporates a user input as part of the mask, the user input is usable at least in part to define a threshold that is variable based on the user input and configured for use in performing a sound decomposition process. The sound decomposition process is performed using the mask to assign portions of sound data to respective ones of a plurality of sources of the sound data.

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: Sound decomposition models are described. In one or more implementations, a plurality of individual models is generated for respective ones of a plurality of sound sources. The plurality of models is collected to form a universal audio model that is configured to support sound decomposition of sound data through use of one or more of the models. The plurality of models is not generated using a sound source that originated at least a portion of the sound data.

Abstract: Methods and systems for non-negative hidden Markov modeling of signals are described. For example, techniques disclosed herein may be applied to signals emitted by one or more sources. The modeling may be constrained according to high level information. In some embodiments, methods and systems may enable the separation of a signal's various components. As such, the systems and methods disclosed herein may find a wide variety of applications. In audio-related fields, for example, these techniques may be useful in music recording and processing, source separation/extraction, noise reduction, teaching, automatic transcription, electronic games, audio search and retrieval, and many other applications.

Abstract: Systems and methods for semi-supervised source separation using non-negative techniques are described. In some embodiments, various techniques disclosed herein may enable the separation of signals present within a mixture, where one or more of the signals may be emitted by one or more different sources. In audio-related applications, for instance, a signal mixture may include speech (e.g., from a human speaker) and noise (e.g., background noise). In some cases, speech may be separated from noise using a speech model developed from training data. A noise model may be created, for example, during the separation process (e.g., “on-the-fly”) and in the absence of corresponding training data.

Abstract: Sound decomposition techniques and user interfaces are described. In one or more implementations, one or more inputs are received via interaction with a representation of sound data in a user interface, the one or more inputs indicating a portion and corresponding intensity of the sound data. The sound data is decomposed according to at least one respective source based at least in part on the selected portion and indicated intensity to guide a learning process used in the decomposing. Other implementations are also contemplated, such as implementations that do not involve an indication of intensity, implementations involving concurrent display of sound data as being associated with respective sources, and so on.

Abstract: Noise robust template matching may be performed. First features of a first signal may be computed. Based at least on a portion of the first features, second features of a second signal may be computed. A new signal may be generated based on at least another portion of the first features and on at least a portion of the second features.

Abstract: Sound feature priority alignment techniques are described. In one or more implementations, features of sound data are identified from a plurality of recordings. Values are calculated for frames of the sound data from the plurality of recordings. The values are based on similarity of the frames of the sound data from the plurality of recordings to each other, the similarity based on the identified features and a priority that is assigned based on the identified features of respective frames. The sound data from the plurality of recordings is then aligned based at least in part on the calculated values.

Abstract: Sound rate modification techniques are described. In one or more implementations, an indication is received of an amount that a rate of output of sound data is to be modified. One or more sound rate rules are applied to the sound data that, along with the received indication, are usable to calculate different rates at which different portions of the sound data are to be modified, respectively. The sound data is then output such that the calculated rates are applied.

Abstract: Sound alignment techniques that employ timing information are described. In one or more implementations, features and timing information of sound data generated from a first sound signal are identified and used to identify features of sound data generated from a second sound signal. The identified features may then be utilized to align portions of the sound data from the first and second sound signals to each other.

Abstract: Sound alignment user interface techniques are described. In one or more implementations, a user interface is output having a first representation of sound data generated from a first sound signal and a second representation of sound data generated from a second sound signal. One or more inputs are received, via interaction with the user interface, that indicate that a first point in time in the first representation corresponds to a second point in time in the second representation. Aligned sound data is generated from the sound data from the first and second sound signals based at least in part on correspondence of the first point in time in the sound data generated from the first sound signal to the second point in time in the sound data generated from the second sound signal.

Abstract: Time interval sound alignment techniques are described. In one or more implementations, one or more inputs are received via interaction with a user interface that indicate that a first time interval in a first representation of sound data generated from a first sound signal corresponds to a second time interval in a second representation of sound data generated from a second sound signal. A stretch value is calculated based on an amount of time represented in the first and second time intervals, respectively. Aligned sound data is generated from the sound data for the first and second time intervals based on the calculated stretch value.

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: Methods and systems for non-negative hidden Markov modeling of signals are described. For example, techniques disclosed herein may be applied to signals emitted by one or more sources. In some embodiments, methods and systems may enable the separation of a signal's various components. As such, the systems and methods disclosed herein may find a wide variety of applications. In audio-related fields, for example, these techniques may be useful in music recording and processing, source extraction, noise reduction, teaching, automatic transcription, electronic games, audio search and retrieval, and many other applications.

Abstract: Methods and systems for non-negative hidden Markov modeling of signals are described. For example, techniques disclosed herein may be applied to signals emitted by one or more sources. The modeling may be constrained according to high level information. In some embodiments, methods and systems may enable the separation of a signal's various components. As such, the systems and methods disclosed herein may find a wide variety of applications. In audio-related fields, for example, these techniques may be useful in music recording and processing, source separation/extraction, noise reduction, teaching, automatic transcription, electronic games, audio search and retrieval, and many other applications.

Abstract: A sound mixture may be received that includes a plurality of sources. A model may be received for one of the source that includes a dictionary of spectral basis vectors corresponding to that one source. At least one feature of the one source in the sound mixture may be estimated based on the model. In some examples, the estimation may be constrained according to temporal data.

Abstract: Systems and methods for semi-supervised source separation using non-negative techniques are described. In some embodiments, various techniques disclosed herein may enable the separation of signals present within a mixture, where one or more of the signals may be emitted by one or more different sources. In audio-related applications, for instance, a signal mixture may include speech (e.g., from a human speaker) and noise (e.g., background noise). In some cases, speech may be separated from noise using a speech model developed from training data. A noise model may be created, for example, during the separation process (e.g., “on-the-fly”) and in the absence of corresponding training data.

Abstract: Methods and systems for non-negative hidden Markov modeling of signals are described. For example, techniques disclosed herein may be applied to signals emitted by one or more sources. In some embodiments, methods and systems may enable the separation of a signal's various components. As such, the systems and methods disclosed herein may find a wide variety of applications. In audio-related fields, for example, these techniques may be useful in music recording and processing, source extraction, noise reduction, teaching, automatic transcription, electronic games, audio search and retrieval, and many other applications.

Abstract: Online source separation may include receiving a sound mixture that includes first audio data from a first source and second audio data from a second source. Online source separation may further include receiving pre-computed reference data corresponding to the first source. Online source separation may also include performing online separation of the second audio data from the first audio data based on the pre-computed reference data.

Abstract: Noise robust template matching may be performed. First features of a first signal may be computed. Based at least on a portion of the first features, second features of a second signal may be computed. A new signal may be generated based on at least another portion of the first features and on at least a portion of the second features.