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.

An apparatus and method for pitch-shifting an audio signal with low complexity are disclosed. The method includes identifying a distance between an audio object included in the audio signal and a listener, checking whether the distance between the audio object and the listener decreases, and performing stepwise stretching pitch-shifting of repeatedly using at least one of frequency components of the audio signal when the distance between the audio object and the listener decreases.

The present disclosure describes techniques for differentiable wavetable synthesizer. The techniques comprise extracting features from a dataset of sounds, wherein the features comprise at least timbre embedding; input the features to the first machine learning model, wherein the first machine learning model is configured to extract a set of N×L learnable parameters, N represents a number of wavetables, and L represents a wavetable length; outputting a plurality of wavetables, wherein each of plurality of wavetables comprises a waveform associated with a unique timbre, the plurality of wavetables form a dictionary, and the plurality of wavetables are portable to perform audio-related tasks.

Technologies are described for identifying familiar or interesting parts of music content by analyzing changes in vocal power using frequency spectrums. For example, a frequency spectrum can be generated from digitized audio. Using the frequency spectrum, the harmonic content and percussive content can be separated. The vocal content can then be separated from the harmonic and/or percussive content. The vocal content can then be processed to identify surge points in the digitized audio. In some implementations, the vocal content is included in the harmonic content during the separation procedure and is then separated from the harmonic content.

An acoustic feedback detection method and device. According to the method, whether acoustic feedback occurs is determined based on a frequency characteristic of an acoustic feedback signal. Specifically, a judgment value is determined using a power peak value and an average peak value, and it is determined whether acoustic feedback occurs in a signal based on a magnitude of the judgment value and a duration of the power peak value. In this case, whether acoustic feedback occurs can be determined based on the frequency characteristic of the signal.

Scalable similarity-based generation of compatible music mixes. Music clips are projected in a pitch interval space for computing musical compatibility between the clips as distances or similarities in the pitch interval space. The distance or similarity between clips reflects the degree to which clips are harmonically compatible. The distance or similarity in the pitch interval space between a candidate music clip and a partial mix can be used to determine if the candidate music clip is harmonically compatible with the partial mix. An indexable feature space may be both beats-per-minute (BPM)-agnostic and musical key-agnostic such that harmonic compatibility can be quickly determined among potentially millions of music clips. A graphical user interface-based user application allows users to easily discover combinations of clips from a library that result in a perceptually high-quality mix that is highly consonant and pleasant-sounding and reflects the principles of musical harmony.

A matrix is generated that stores sinusoidal components evaluated for a given sample rate corresponding to the matrix. The matrix is then used to convert an audio signal to chroma vectors representing of a set of “chromae” (frequencies of interest). The conversion of an audio signal portion into its chromae enables more meaningful analysis of the audio signal than would be possible using the signal data alone. The chroma vectors of the audio signal can be used to perform analyzes such as comparisons with the chroma vectors obtained from other audio signals in order to identify audio matches.

An illumination device includes a signal reception unit capable of receiving an audio signal from outside, a musical piece extraction unit capable of extracting continuous consonant sounds in the audio signal as a musical piece, a performance state detection unit capable of detecting start/end of performance of the musical piece according to a result of extraction by the musical piece extraction unit, a first illumination lamp capable of radiating ultraviolet rays, a second illumination lamp capable of radiating white light, and an illumination control unit capable of controlling on/off of the first illumination lamp, and of controlling on/off and illuminance of the second illumination lamp, The first and the second illumination lamps are turned on in response to detection of the start of performance, and the first and the second illumination lamps are turned off in response to detection of the end of performance.

A method including decomposing a magnitude part of a signal spectrum of a mixture signal into spectral components, each spectral component including a frequency part and a time activation part; and clustering the spectral components to obtain one or more clusters of spectral components, wherein the clustering of the spectral components is computed in the time domain.

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.