A Hidden Markov Model-Based Performance Recognition System for Marching Wind Bands

This paper explores the automatic recognition of marching band performances using advanced music information retrieval techniques. Music, a crucial medium for emotional expression and cultural exchange, greatly benefits from the harmonic backing provided by marching wind orchestras. Identifying these performances manually is both time-consuming and labor-intensive, particularly for non-professionals. This study addresses this challenge by leveraging Hidden Markov Models (HMM) and improved Pitch Class Profile (PCP) features to automate the recognition process. The research also explores the system's performance on real-world audio recordings with background noise and microphone variations. By dividing the audio signal into frames and transforming it to the frequency domain, the PCP feature vectors are extracted and used within the HMM framework. Experimental results demonstrate that the proposed method significantly enhances recognition accuracy compared to traditional PCP features and template matching models. The study identifies challenges in distinguishing similar tonal values, such as F-major and D-minor, which affect recognition rates. Additionally, the research highlights the importance of addressing background noise and microphone variations in real-world applications. Ethical considerations regarding privacy and intellectual property rights are also discussed. This research establishes a comprehensive system for automatic marching band performance recognition, contributing to advancements in music information retrieval and analysis.

@article{Jiang2024,
  title     = {A Hidden Markov Model-Based Performance Recognition System for Marching Wind Bands},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Wei Jiang},
  doi       = {10.14569/IJACSA.2024.0150841},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150841}
}