Computational Nanotechnology Issue №1 — 2024

Neural Networks in the Task of Genre Classification of Musical Compositions
( Pp. 135-150)

Belenkiy Mikhail A. Grineva Natalia V.

DOI: 10.33693/2313-223X-2024-11-1-135-150

Belenkiy Mikhail A. student, Faculty of Information Technology and Big Data Analysis
Financial University under the Government of the Russian Federation
Moscow, Russian Federation Grineva Natalia V. Cand. Sci. (Econ.), Associate Professor; associate professor, Department of Data Analysis and Machine Learning; Financial University under the Government of the Russian Federation; Moscow, Russian Federation

Abstract:

This study investigates the application of neural networks in the task of classifying audio signals into ten different genres. The peculiarities of processing audio signals in the digital environment are examined, along with the relationship between Fourier transformation and spectrograms, and the characteristics of audio signals. Neural network training was conducted using the GTZAN dataset, which contains 1000 compositions. Four comparable datasets were formed based on this dataset, and the performance of three neural network architectures – convolutional, recurrent, and multilayer perceptron – was evaluated on each of them. The practical significance of this work lies in the possibility of forming musical recommendations and organizing music. The goal of the study is to develop a classifier that could accurately determine the probability of a composition belonging to one of the ten genres.

How to Cite:

Belenkiy M.A., Grineva N.V. Neural Networks in the Task of Genre Classification of Musical Compositions. Computational Nanotechnology. 2024. Vol. 11. No. 1. Pp. 135–150. (In Rus.) DOI: 10.33693/2313-223X-2024-11-1-135-150. EDN: EIJGDK

Reference list:

Labintsev A., Khasanshin I., Balashov D. et al. Recognition Punches in Karate Using Acceleration Sensors and Convolution Neural Networks // IEEE Access. 2021. Vol. 9. Pp. 138106-138119. DOI: 10.1109/ACCESS.2021.3118038.
Dipankar Das, Anup Kumar Kolya, Abhishek Basu, Soham Sarkar. Computational intelligence applications for text and sentiment data analysis. Academic Press, 2023. P. 252. ISBN: 9780323905350. DOI: 10.1016/B978-0-32-390535-0.00007-0.
Gated recurrent unit networks: Efficient neural architecture for sequential data. URL: https://askanydifference.com/gated-recurrent-unit-networks/ (дата обращения: 14.12.2023).
LSTM и GRU. URL: https://habr.com/ru/companies/mvideo/articles/780774/ (дата обращения: 03.12.2023).
NLP с нуля: машинный перевод при помощи seq2seq с механизмом внимания. URL: https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html#loading-data-files (дата обращения: 01.12.2023).
Sansano E., Montoliu R., Belmonte Fernández Ó. A study of deep neural networks for human activity recognition // Computational Intelligence. 2020. No. 36. Pp. 1113–1139. DOI: 10.1111/coin.12318.
Shahriar Akter, Saradhi Motamarri, Umme Hani. et al. Building dynamic service analytics capabilities for the digital marketplace // Journal of Business Research. 2020. Vol. 118. Pp. 177–188. ISSN: 0148-2963. DOI: 10.1016/j.jbusres.2020.06.016.
Введение в Text Mining и предварительная обработка текстовых данных. URL: https://nagornyy.me/it/vvedenie-v-text-mining-i-predvaritelnaia-obrabotka-tekstovykh-dannykh/ (дата обращения: 05.12.2023).
Введение в глубокое обучение: пошаговое руководство. URL: https://pythonist.ru/vvedenie-v-glubokoe-obuchenie-po-shagam/? (дата обращения: 02.12.2023).
Визуализируя нейронный машинный перевод (seq2seq модели с механизмом внимания). URL: https://habr.com/ru/post/486158/ (дата обращения: 03.12.2023).
Как вывести карточку товара в топ на Wildberries. URL: https://ritm-z.ru/blog/Ritm-Z/kak-vyvesti-kartochku-tovara-v-top-na-wildberries/ (дата обращения: 02.12.2023).
Макарова Е.С., Будаев Е.С. Интеллектуальный анализ отзывов клиентов с применением модели seq2seq // Интернаука: электрон. научн. журн. 2022. № 22 (245). URL: https://internauka.org/journal/science/internauka/245 (дата обращения: 14.12.2023). DOI: 10.32743/26870142.2022.22.245.342556.
Маркетплейсы: определение, типы, площадки. URL: https://investprofit.info/marketplace/? (дата обращения: 02.12.2023).

Keywords:

GTZAN, audio signal, mel spectrogram, spectrum, Fourier transform, GTZAN, multilayer perceptron (MLP), convolutional neural network (CNN), genre classification task.