DeepSRGM -- Sequence Classification and Ranking in Indian Classical Music with Deep Learning

A vital aspect of Indian Classical Music (ICM) is Raga, which serves as a melodic framework for compositions and improvisations alike. Raga Recognition is an important music information retrieval task in ICM as it can aid numerous downstream applications ranging from music recommendations to organizing huge music collections. In this work, we propose a deep learning based approach to Raga recognition. Our approach employs efficient pre possessing and learns temporal sequences in music data using Long Short Term Memory based Recurrent Neural Networks (LSTM-RNN). We train and test the network on smaller sequences sampled from the original audio while the final inference is performed on the audio as a whole. Our method achieves an accuracy of 88.1% and 97 % during inference on the Comp Music Carnatic dataset and its 10 Raga subset respectively making it the state-of-the-art for the Raga recognition task. Our approach also enables sequence ranking which aids us in retrieving melodic patterns from a given music data base that are closely related to the presented query sequence

Multimodal Deep Learning Architecture for Hindustani Raga Classification

In this paper, our key aspect is the design of a deep learning architecture for the classification of Hindustani (classical North Indian music) ragas (music modes). In an attempt to address this task, we propose a modular deep learning architecture designed to process data from two modalities, comprising audio recordings and metadata. Our bipolar classifier utilizes convolutional and feed forward neural networks and incorporates spectral information of audio data and metadata descriptors tailored to the peculiar melodic characteristics of Hindustani music. In specific, audio recordings as well as manually annotated and automatically extracted metadata were utilized for audio samples of both Hindustani improvisations and compositions available in the Saraga open dataset of Indian art music. Experiments are conducted on two Hindustani ragas, namely Yaman and Bhairavi. Results indicate that the integration of multimodal data increases the classification accuracy of the classifier in comparison to simply using audio features. Additionally, for the specific task of raga classification the use of the swaragram feature, which is customized for Hindustani music, outperforms the effectiveness of audio features that are commonly used in Eurocentric music genres