YMIR: A new Benchmark Dataset and Model for Arabic Yemeni Music Genre Classification Using Convolutional Neural Networks

• URL: http://arxiv.org/abs/2604.05011v1
• Date: Mon, 06 Apr 2026 15:04:24 GMT
• Title: YMIR: A new Benchmark Dataset and Model for Arabic Yemeni Music Genre Classification Using Convolutional Neural Networks
• Abstract summary: We introduce the Yemeni Music Information Retrieval (YMIR) dataset, which contains 1,475 carefully selected audio clips covering five traditional Yemeni genres.<n>We also propose the Yemeni Music Classification Model (YMCM), a convolutional neural network (CNN)-based system designed to classify music genres from time-frequency features.
• Score: 1.1744028458220428
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic music genre classification is a major task in music information retrieval; however, most current benchmarks and models have been developed primarily for Western music, leaving culturally specific traditions underrepresented. In this paper, we introduce the Yemeni Music Information Retrieval (YMIR) dataset, which contains 1,475 carefully selected audio clips covering five traditional Yemeni genres: Sanaani, Hadhrami, Lahji, Tihami, and Adeni. The dataset was labeled by five Yemeni music experts following a clear and structured protocol, resulting in strong inter-annotator agreement (Fleiss kappa = 0.85). We also propose the Yemeni Music Classification Model (YMCM), a convolutional neural network (CNN)-based system designed to classify music genres from time-frequency features. Using a consistent preprocessing pipeline, we perform a systematic comparison across six experimental groups and five different architectures, resulting in a total of 30 experiments. Specifically, we evaluate several feature representations, including Mel-spectrograms, Chroma, FilterBank, and MFCCs with 13, 20, and 40 coefficients, and benchmark YMCM against standard models (AlexNet, VGG16, MobileNet, and a baseline CNN) under the same experimental conditions. The experimental findings reveal that YMCM is the most effective, achieving the highest accuracy of 98.8% with Mel-spectrogram features. The results also provide practical insights into the relationship between feature representation and model capacity. The findings establish YMIR as a useful benchmark and YMCM as a strong baseline for classifying Yemeni music genres.

Related papers

• FIGMA: Towards FIne-Grained Music retrievAl [65.98380295254817]
  Retrieving music using natural language descriptions has improved with contrastive audio-text models such as CLAP, but current systems remain limited to coarse semantic queries.<n>We show that this limitation stems from the contrastive learning objective itself: despite being trained on long captions, CLAP-based models effectively utilize only the first few tokens.<n>We propose FIGMA, a multi-view contrastive architecture that addresses this limitation by jointly optimizing global audio-text alignment and frame-level, token-wise alignment.
  arXiv  Detail & Related papers  (2026-06-04T18:05:39Z)
• Music Genre Classification: A Comparative Analysis of Classical Machine Learning and Deep Learning Approaches [0.0]
  We construct a novel dataset of approximately 8,000 labeled 30-second audio clips spanning eight Nepali music genres.<n>We offer a culturally grounded interpretation of misclassification patterns that reflects genuine overlaps in Nepal's musical traditions.
  arXiv  Detail & Related papers  (2026-03-16T15:43:48Z)
• Towards an AI Musician: Synthesizing Sheet Music Problems for Musical Reasoning [69.78158549955384]
  We introduce a novel approach that treats core music theory rules, such as those governing beats and intervals, as programmatic functions.<n>This approach generates verifiable sheet music questions in both textual and visual modalities.<n> Evaluation results on SSMR-Bench highlight the key role reasoning plays in interpreting sheet music.
  arXiv  Detail & Related papers  (2025-09-04T09:42:17Z)
• Universal Music Representations? Evaluating Foundation Models on World Music Corpora [65.72891334156706]
  Foundation models have revolutionized music information retrieval, but questions remain about their ability to generalize.<n>This paper presents a comprehensive evaluation of five state-of-the-art audio foundation models across six musical corpora.
  arXiv  Detail & Related papers  (2025-06-20T15:06:44Z)
• Aligning Text-to-Music Evaluation with Human Preferences [63.08368388389259]
  We study the design space of reference-based divergence metrics for evaluating generative acoustic text-to-music (TTM) models.<n>We find that not only is the standard FAD setup inconsistent on both synthetic and human preference data, but that nearly all existing metrics fail to effectively capture desiderata.<n>We propose a new metric, the MAUVE Audio Divergence (MAD), computed on representations from a self-supervised audio embedding model.
  arXiv  Detail & Related papers  (2025-03-20T19:31:04Z)
• Audio Processing using Pattern Recognition for Music Genre Classification [0.0]
  This project explores the application of machine learning techniques for music genre classification using the GTZAN dataset. Motivated by the growing demand for personalized music recommendations, we focused on classifying five genres-Blues, Classical, Jazz, Hip Hop, and Country. The ANN model demonstrated the best performance, achieving a validation accuracy of 92.44%.
  arXiv  Detail & Related papers  (2024-10-19T05:44:05Z)
• Music Genre Classification: A Comparative Analysis of CNN and XGBoost Approaches with Mel-frequency cepstral coefficients and Mel Spectrograms [0.0]
  This study investigates the performances of three models: a proposed convolutional neural network (CNN), the VGG16 with fully connected layers (FC), and an eXtreme Gradient Boosting (XGBoost) approach on different features. The results show that the MFCC XGBoost model outperformed the others. Furthermore, applying data segmentation in the data preprocessing phase can significantly enhance the performance of the CNNs.
  arXiv  Detail & Related papers  (2024-01-09T01:50:31Z)
• MARBLE: Music Audio Representation Benchmark for Universal Evaluation [79.25065218663458]
  We introduce the Music Audio Representation Benchmark for universaL Evaluation, termed MARBLE. It aims to provide a benchmark for various Music Information Retrieval (MIR) tasks by defining a comprehensive taxonomy with four hierarchy levels, including acoustic, performance, score, and high-level description. We then establish a unified protocol based on 14 tasks on 8 public-available datasets, providing a fair and standard assessment of representations of all open-sourced pre-trained models developed on music recordings as baselines.
  arXiv  Detail & Related papers  (2023-06-18T12:56:46Z)
• A Dataset for Greek Traditional and Folk Music: Lyra [69.07390994897443]
  This paper presents a dataset for Greek Traditional and Folk music that includes 1570 pieces, summing in around 80 hours of data. The dataset incorporates YouTube timestamped links for retrieving audio and video, along with rich metadata information with regards to instrumentation, geography and genre.
  arXiv  Detail & Related papers  (2022-11-21T14:15:43Z)
• MATT: A Multiple-instance Attention Mechanism for Long-tail Music Genre Classification [1.8275108630751844]
  Imbalanced music genre classification is a crucial task in the Music Information Retrieval (MIR) field. Most of the existing models are designed for class-balanced music datasets. We propose a novel mechanism named Multi-instance Attention (MATT) to boost the performance for identifying tail classes.
  arXiv  Detail & Related papers  (2022-09-09T03:52:44Z)
• A dataset and classification model for Malay, Hindi, Tamil and Chinese music [7.35996217853436]
  We use this new dataset to train different classification models to distinguish the origin of the music in terms of these ethnic groups. The classification models were optimized by exploring the use of different musical features as the input.
  arXiv  Detail & Related papers  (2020-09-09T06:17:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.