Authors - Ananya Kale, Aditi Jaikar, Shravika Hamjade, Neeta Maitre, Rashmi Apte, Mangesh Bedekar Abstract - Singer identification is a challenging task because of pitch and me lodic variations, tempo, vibrato, and adaptive singing styles. This paper propos es a novel approach towards singer identification and classification by adapting a model originally meant for speaker recognition. Specifically, this work utiliz es vector representations extracted from a pretrained Speech Brain Emphasized Channel Attention, Propagation and Aggregation in Time Delay Neural Net work (ECAPA-TDNN) model. The research pipeline processes a custom curated dataset of four prominent Indian playback singers into fixed, 8 second audio clips, with mono channel sampled at 16 kHz and exported as wav files. The Speech Brain Emphasized Channel Attention, Propagation and Aggrega tion (ECAPA) encoder transforms these labelled clips into fixed embeddings which are unique vector representations of voice characteristics of each audio clips. A suite of classical machine learning classifiers is trained on these em beddings. The study evaluates four of them namely, Logistic Regression, Sup port Vector Machines, Random Forests, and a Multi-Layer Perceptron (MLP). The MLP achieved the highest accuracy of 99.38% on held-out test data. Sup porting this result, both confusion matrix analysis and t-SNE projection clearly demonstrate clear cluster separation based on individual singer identities. These findings thus collectively validate that ECAPA embeddings contain sufficient identity-bearing structure on a singing voice. This analysis thus concludes that adaptation of speaker recognition models with appropriate classifiers is a great ly effective and efficient approach for singer identification.
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