A dual sound source audio data processing method and apparatus are provided. The method includes obtaining audio data of a song pair including a first song and a second song, the first and second songs having a same accompaniment audio but different voice audio. The audio data is decoded to obtain first mono audio data corresponding to the first song and second mono audio data corresponding to the second song. The first and second mono audio data are combined to one piece of two-channel audio data including a left audio channel and a right audio channel. A play time of the two-channel audio data is divided into play periods, and energy suppression is selectively performed on the left audio channel and the right audio channel in different play periods.

Embodiments of the present invention relate to automatically identifying structures of a music stream. A segment structure may be generated that visually indicates repeating segments of a music stream. To generate a segment structure, a feature that corresponds to a music attribute from a waveform corresponding to the music stream is extracted from a waveform, such as an input signal. Utilizing a signal segmentation algorithm, such as a Variable Markov Oracle (VMO) algorithm, a symbolized signal, such as a VMO structure, is generated. From the symbolized signal, a matrix is generated. The matrix may be, for instance, a VMO-SSM. A segment structure is then generated from the matrix. The segment structure illustrates a segmentation of the music stream and the segments that are repetitive.

A derivative track for an audio track may be generated. An audio track duration of the audio track may be partitioned into partitions of a partition size. A current partition may be compared to remaining partitions of the audio track. Audio information for the current partition may be correlated to audio information for remaining partitions to determine a correlated partition for the current partition from among the remaining partitions of the track duration. The correlated partition determined may be identified as most likely to represent the same sound as the current partition. This comparison process may be performed iteratively for individual ones of the remaining partitions. One or more regions of the audio track may be identified. Individual regions may include multiple correlated partitions that are temporally adjacent along the audio track duration. One or more partitions within one or more regions may be removed to generate the derivative track.

Embodiments of the present invention relate to detecting irregularities in audio, such as music. An input signal corresponding to an audio stream is received. The input signal is transformed from a time domain into a frequency domain to generate a plurality of frames that each comprises frequency information for a portion of the input signal. An irregular event in a portion of the input signal corresponding to a set of frames in the plurality of frames is identified based on a comparison of frequency information of the set of frames to the frequency information of other sets of frames of the plurality of frames. This allows an indication of the irregular event to be provided, or for the input signal to be automatically synchronized to a multimedia event.

Embodiments of the present invention relate to detecting irregularities in audio, such as music. An input signal corresponding to an audio stream is received. The input signal is transformed from a time domain into a frequency domain to generate a plurality of frames that each comprises frequency information for a portion of the input signal. An irregular event in a portion of the input signal corresponding to a set of frames in the plurality of frames is identified based on a comparison of frequency information of the set of frames to the frequency information of other sets of frames of the plurality of frames. This allows an indication of the irregular event to be provided, or for the input signal to be automatically synchronized to a multimedia event.

Embodiments of the present invention relate to automatically identifying structures of a music stream. A segment structure may be generated that visually indicates repeating segments of a music stream. To generate a segment structure, a feature that corresponds to a music attribute from a waveform corresponding to the music stream is extracted from a waveform, such as an input signal. Utilizing a signal segmentation algorithm, such as a Variable Markov Oracle (VMO) algorithm, a symbolized signal, such as a VMO structure, is generated. From the symbolized signal, a matrix is generated. The matrix may be, for instance, a VMO-SSM. A segment structure is then generated from the matrix. The segment structure illustrates a segmentation of the music stream and the segments that are repetitive.

A system and method that quantifies a sound into dynamic pitch-based graphs that correlate to the pitch frequencies of the sound. The system records a sound, such as musical notes. A pitch detection algorithm identifies and quantifies the pitch frequencies of the notes. The algorithm analyzes the pitch frequencies, and graphically displays the pitch frequency and notes in real time as fluctuating circles, rectangular bars, and lines that represent variances in pitch. The algorithm comprises a modified Type 2 Normalized Square Difference Function that transforms the musical notes into the pitch frequencies. The Type 2 Normalized Square Difference Function analyzes the peaks of the pitch frequency to arrive at a precise pitch frequency, such as 440 Hertz. A Lagrangian interpolation enables comparative analysis and teaching of the pitches and notes. The algorithm also performs transformations and heuristic comparisons to generate the real time graphical representation of the pitch frequency.

A method for extracting the most representative segments of a musical composition, represented by an audio signal, according to which the audio signal is preprocessed by a set of preprocessors, each if which is adapted to identify a rhythmic pattern. The output of the preprocessors that provided the most periodic or rhythmical patterns in the musical composition selected and the musical composition is divided into bars with rhythmic patterns, while iteratively checking and scoring their quality and detecting a section that is a sequence of bars with score above a predetermined threshold. Checking and scoring is iteratively repeated until all sections are detected. Then similarity matrices between all bars that belong to the musical composition are constructed, based on MFCCs of the processed sound, chromograms and the rhythmic patterns. Then equivalent classes of similar sections are extracted along the musical composition. Substantial transitions between sections represented as blocks in the similarity matrices are collected and a representative segment is selected from each class with the highest number of sections.

The present application discloses and describes neurostimulation systems and methods that include, among other features, (i) neural stimulation through audio with dynamic modulation characteristics, (ii) audio content serving and creation based on modulation characteristics, (iii) extending audio tracks while avoiding audio discontinuities, and (iv) non-auditory neurostimulation and methods, including non-auditory neurostimulation for anesthesia recovery.

A device to determine a number of beats per bar from a music data includes at least one processor configured to calculate a weighted average beat level waveform from a first beat level waveform obtained for a first frequency band and a second beat level waveform obtained for a second frequency band; calculate autocorrelation on the weighted average beat level waveform by varying an amount of a shift interval for the autocorrelation; determine a plurality of the shift intervals at which correlation values of the autocorrelation are n highest, where n is a positive integer greater than or equal to 2; and determine the number of beats per bar based on the determined plurality of the shift intervals at which the correlation values of the autocorrelation are n highest.