Genre based emotion annotation for music in noisy environment

The music listened by human is sometimes exposed to noise. For example, background noise usually exists when listening to music in broadcasts or lives. The noise will worsen the performance in various music emotion recognition systems. To solve the problem, this work constructs a robust system for music emotion classification in a noisy environment. Furthermore, the genre is considered when determining the emotional label for the song. The proposed system consists of three major parts, i.e. subspace based noise suppression, genre index computation, and support vector machine (SVM). Firstly, the system uses noise suppression to remove the noise content in the signal. After that, acoustical features are extracted from each music clip. Next, a dictionary is constructed by using songs that cover a wide range of genres, and it is adopted to implement sparse coding. Via sparse coding, data can be transformed to sparse coefficient vectors, and this paper computes genre indexes for the music genres based on the sparse coefficient vector. The genre indexes are regarded as combination weights in the latter phase. At the training stage of the SVM, this paper train emotional models for each genre. At the prediction stage, the predictions that obtained by emotional models in each genre are weighted combined across all genres using the genre indexes. Finally, the proposed system annotates multiple emotional labels for a song based on the combined prediction. The experimental result shows that the system can achieve a good performance in both normal and noisy environments.