1D CNN Architectures for Music Genre Classification
It addresses music genre recognition for audio analysis applications, but is incremental as it builds on existing 1D CNN approaches.
This paper tackled music genre classification by proposing a 1D residual CNN architecture that processes raw audio signals, achieving 80.93% mean accuracy on a dataset of 1,000 audio clips and outperforming other 1D CNN methods.
This paper proposes a 1D residual convolutional neural network (CNN) architecture for music genre classification and compares it with other recent 1D CNN architectures. The 1D CNNs learn a representation and a discriminant directly from the raw audio signal. Several convolutional layers capture the time-frequency characteristics of the audio signal and learn various filters relevant to the music genre recognition task. The proposed approach splits the audio signal into overlapped segments using a sliding window to comply with the fixed-length input constraint of the 1D CNNs. As a result, music genre classification can be carried out on a single audio segment or on the aggregation of the predictions on several audio segments, which improves the final accuracy. The performance of the proposed 1D residual CNN is assessed on a public dataset of 1,000 audio clips. The experimental results have shown that it achieves 80.93% of mean accuracy in classifying music genres and outperforms other 1D CNN architectures.