A method for determining a compact semantic representation of a digital audio signal using a computer-based system, the method comprising: providing a digital audio signal; calculating, using a digital signal processor module, a low-level feature matrix from said digital audio signal, said 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 said low-level feature matrix, said high-level feature matrix comprising numerical values corresponding to a high-level audio feature; calculating, using a feature-specific extractor module, a number n f of high-level feature vectors from said 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 n f of high-level feature values from said number n f of high-level feature vectors; wherein each high-level feature value represents a musical or emotional characteristic of said digital audio signal; and calculating a descriptor vector by concatenating said number n f of high-level feature values characterized in that said general extractor module uses a pre-trained Convolutional Neural Network, CNN, model pre-trained in isolation from the rest of the modules as a musical genre classifier model; wherein the architecture of said CNN model comprises: an input block configured for normalizing said low-level feature matrix using a batch normalization layer; followed by four consecutive convolutional blocks; and an output layer. providing a digital audio signal; calculating, using a digital signal processor module, a low-level feature matrix from said digital audio signal, said 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 said low-level feature matrix, said high-level feature matrix comprising numerical values corresponding to a high-level audio feature; calculating, using a feature-specific extractor module, a number n f of high-level feature vectors from said 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 n f of high-level feature values from said number n f of high-level feature vectors; wherein each high-level feature value represents a musical or emotional characteristic of said digital audio signal; and calculating a descriptor vector by concatenating said number n f of high-level feature values characterized in that said general extractor module uses a pre-trained Convolutional Neural Network, CNN, model pre-trained in isolation from the rest of the modules as a musical genre classifier model; wherein the architecture of said CNN model comprises: an input block configured for normalizing said low-level feature matrix using a batch normalization layer; followed by four consecutive convolutional blocks; and an output layer. 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 said low-level feature matrix, said high-level feature matrix comprising numerical values corresponding to a high-level audio feature; calculating, using a feature-specific extractor module, a number n f of high-level feature vectors from said 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 n f of high-level feature values from said number n f of high-level feature vectors; wherein each high-level feature value represents a musical or emotional characteristic of said digital audio signal; and calculating a descriptor vector by concatenating said number n f of high-level feature values characterized in that said general extractor module uses a pre-trained Convolutional Neural Network, CNN, model pre-trained in isolation from the rest of the modules as a musical genre classifier model; wherein the architecture of said CNN model comprises: an input block configured for normalizing said low-level feature matrix using a batch normalization layer; followed by four consecutive convolutional blocks; and an output layer. an input block configured for normalizing said low-level feature matrix using a batch normalization layer; followed by four consecutive convolutional blocks; and an output layer.