Methods and systems for determining compact semantic representations of digital audio signals

A method for determining a compact semantic representation of a digital audio signal using computer-based system, the method comprising:

• providing a digital audio signal;
• calculating, using a digital signal processor module, a low-level feature matrix from the digital audio signal, the low-level feature matrix comprising numerical values corresponding to a low-level audio feature in a temporal sequence;
• calculating, using a general extractor module, a high-level feature matrix from the low-level feature matrix, the high-level feature matrix comprising numerical values corresponding to a high-level audio feature;
• calculating, using a feature-specific extractor module, a number nf of high-level feature vectors from the high-level feature matrix, each high-level feature vector comprising numerical values corresponding to a high-level audio feature;
• calculating, using a feature-specific regressor module, a number nf of high-level feature values from the number nf of high-level feature vectors;
• wherein each high-level feature value represents a musical or emotional characteristic of the digital audio signal;
• and
• calculating a descriptor vector by concatenating the number nf of high-level feature values.

A method for determining a compact semantic representation of a digital audio signal using computer-based system, the method comprising:

• providing a digital audio signal;
• calculating, using a low-level feature extractor module, from the digital audio signal , a Mel-spectrogram, and a Mel Frequency Cepstral Coefficients, MFCC, matrix;
• processing, using a low-level feature pre-processor module the Mel-spectrogram and MFCC matrix, wherein the Mel-spectrogram is subjected separately to at least a Multi Auto Regression Analysis, MARA, process and a Dynamic Histogram, DH, process, and the MFCC matrix is subjected separately to at least an Auto Regression Analysis, ARA, process and a MARA process, wherein the output of each MARA process is a first order multivariate autoregression matrix, the output of each ARA process is a third order autoregression matrix, and the output of each DH process is a dynamic histogram matrix;
• and
• calculating, using an ensemble learning module, a number nf of high-level feature values by:
• feeding the output matrices from the low-level feature pre-processor module as a group parallelly into a number nf of ensemble learning blocks within the ensemble learning module, each ensemble learning block further comprising a number nGP of parallelly executed Gaussian Processes, GPs, wherein each of the GPs receives at least one of the output matrices and outputs a predicted high-level feature value, and
• picking, as the output of each ensemble learning block, the best candidate from the predicted high-level feature values, using statistical data, as one of the number nf of high-level feature values, wherein each high-level feature value represents a musical or emotional characteristic of the digital audio signal;
• and
• calculating a descriptor vector by concatenating the number nf of high-level feature values.