Iranian Modal Music (Dastgah) detection using deep neural networks
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In this work, several deep neural networks are implemented to recognize Iranian modal music in seven high correlated categories. The best model, which achieved 92 percent overall accuracy, uses an architecture inspired by autoencoder, including BiLSTM and BiGRU layers. This model is trained using the Nava dataset, with 1786 records and up to 55 hours of music played solo by Kamanche, Tar, Setar, Reed, and Santoor (Dulcimer). Features that have been studied through this research contain MFCC, Chroma CENS, and Mel spectrogram. The results indicate that MFCC carries more valuable information for detecting Iranian modal music (Dastgah) than other sound representations. Moreover, the architecture, which is inspired by autoencoder, is robust in distinguishing high correlated data like Dastgahs. It also shows that because of the precise order in Iranian Dastgah Music, Bidirectional Recurrent networks are more efficient than any other networks that have been implemented in this study.
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