Bangla Music Genre Classification Using Bidirectional LSTMS

TL;DR

This paper presents a novel Bangla music genre classification system using bidirectional LSTM networks and MFCC features, achieving 78% accuracy to improve music organization and retrieval.

Contribution

It introduces a new Bangla music dataset and applies a bidirectional LSTM model for genre classification, which is a novel approach in this context.

Findings

Achieved 78% classification accuracy.

Demonstrated effectiveness of MFCC features with LSTM.

Showed potential for improved music retrieval systems.

Abstract

Bangla music is enrich in its own music cultures. Now a days music genre classification is very significant because of the exponential increase in available music, both in digital and physical formats. It is necessary to index them accordingly to facilitate improved retrieval. Automatically classifying Bangla music by genre is essential for efficiently locating specific pieces within a vast and diverse music library. Prevailing methods for genre classification predominantly employ conventional machine learning or deep learning approaches. This work introduces a novel music dataset comprising ten distinct genres of Bangla music. For the task of audio classification, we utilize a recurrent neural network (RNN) architecture. Specifically, a Long Short-Term Memory (LSTM) network is implemented to train the model and perform the classification. Feature extraction represents a foundational stage in audio data processing. This study utilizes Mel-Frequency Cepstral Coefficients (MFCCs) to transform raw audio waveforms into a compact and representative set of features. The proposed framework facilitates music genre classification by leveraging these extracted features. Experimental results demonstrate a classification accuracy of 78%, indicating the system's strong potential to enhance and streamline the organization of Bangla music genres.