Audio fingerprinting includes obtaining audio samples of a piece of audio, generating frequency representations of the audio samples, identifying increasing and decreasing energy regions in frequency bands of the frequency representations, and generating hashes of features of the piece of audio. Each hash of features corresponds to portions of the identified energy regions appearing in a respective time window. Each feature is defined as a numeric value that encodes information representing: a frequency band of an energy region appearing in the respective time window, whether the energy region appearing in the respective time window is an increasing energy region or whether the energy region appearing in the respective time window is a decreasing energy region, and a placement of the energy region appearing in the respective time window.

A method comprising determining an envelope of an audio file based on a double-windowing analysis of the audio file.

A method comprising determining an envelope of an audio file based on a double-windowing analysis of the audio file.

A system is presented for generation of output sounds having psychoacoustic qualities comparable to input sound or sounds. Short term and intermediate term features are computed for each input sound, sound components are clustered, filtered, and scored; and a prediction learning system is trained on the probabilities of classes of regions over time. A decoder can make use of this information to generate outputs that sound similar to, but not the same as, the input sound or sounds. The method and apparatus can be operated with no special training.

Techniques are described for real-time converting audio into digital musical notation. In an implementation, the process receives a sequence of samples of an audio stream in real time. Based on the sequence of samples, the process generates a window set of note event probability values. The process excludes from the window set of event probability values a leading set of event probability values and a trailing set of event probability values, thereby generating a filtered window set of event probability values. Based on the filtered window set of event probability values, the process determines a sequence set of note-on and note-off events.

An apparatus for analysing a magnitude spectrogram of an audio signal is provided. The apparatus includes a frequency change determiner being configured to determine a change of a frequency for each time-frequency bin of a plurality of time-frequency bins of the magnitude spectrogram of the audio signal depending on the magnitude spectrogram of the audio signal. Moreover, the apparatus includes a classifier being configured to assign each time-frequency bin of the plurality of time-frequency bins to a signal component group of two or more signal component groups depending on the change of the frequency determined for the time-frequency bin.

Multiple audio files may be synchronized using harmonic sound included in audio content obtained from audio tracks. Individual audio tracks are partitioned into multiple temporal windows of a first and second temporal window length. Individual audio waveforms for individual temporal windows of the first and second window length are transformed into frequency space in which energy is represented as a function of frequency. Individual pitches and magnitudes of harmonic sound determined for individual temporal windows may be compared using a multi-resolution framework to correlate pitches and harmonic energy of multiple audio tracks to one another.

Audio fingerprinting includes obtaining audio samples of a piece of audio, generating frequency representations of the audio samples, identifying increasing and decreasing energy regions in frequency bands of the frequency representations, and generating hashes of features of the piece of audio. Each hash of features corresponds to portions of the identified energy regions appearing in a respective time window. Each feature is defined as a numeric value that encodes information representing: a frequency band of an energy region appearing in the respective time window, whether the energy region appearing in the respective time window is an increasing energy region or whether the energy region appearing in the respective time window is a decreasing energy region, and a placement of the energy region appearing in the respective time window.

Audio fingerprinting includes obtaining audio samples of a piece of audio, generating frequency representations of the audio samples, identifying increasing and decreasing energy regions in frequency bands of the frequency representations, and generating hashes of features of the piece of audio. Each hash of features corresponds to portions of the identified energy regions appearing in a respective time window. Each feature is defined as a numeric value that encodes information representing: a frequency band of an energy region appearing in the respective time window, whether the energy region appearing in the respective time window is an increasing energy region or whether the energy region appearing in the respective time window is a decreasing energy region, and a placement of the energy region appearing in the respective time window.

Multiple audio files may be synchronized using harmonic sound included in audio content obtained from audio tracks. Individual audio tracks are partitioned into multiple temporal windows of a first and second temporal window length. Individual audio waveforms for individual temporal windows of the first and second window length are transformed into frequency space in which energy is represented as a function of frequency. Individual pitches and magnitudes of harmonic sound determined for individual temporal windows may be compared using a multi-resolution framework to correlate pitches and harmonic energy of multiple audio tracks to one another.