用小波变换分析爱尔兰民谣音频，精准识别现场录音中的曲目。
Wavelet-Based Time-Frequency Fingerprinting for Feature Extraction of Traditional Irish Music
- 通过连续小波变换提取音频时频特征，实现高精度匹配。
- 在真实录音与合成乐谱间识别准确率显著优于传统方法。
- 模型可拓展至脑电图和金融数据等多领域时序分析。
本文提出一种基于小波变换的时频指纹技术，用于时间序列特征提取，重点解决现场录制的传统爱尔兰民谣音频识别问题。通过连续小波变换提取频谱特征，并利用小波相干性分析比较实录音频频谱图与由ABC记谱法生成的合成曲目。实验表明，该方法能高效准确地识别出实际录音中的曲目。研究还评估了小波相干模型的性能，凸显其在时频分解中的优势。此外，本文探讨并部署该模型于非音乐领域，包括脑电图信号分析与金融时间序列预测。
原文摘要 · Abstract (English)
This work presents a wavelet-based approach to time-frequency fingerprinting for time series feature extraction, with a focus on audio identification from live recordings of traditional Irish tunes. The challenges of identifying features in time-series data are addressed by employing a continuous wavelet transform to extract spectral features and wavelet coherence analysis is used to compare recorded audio spectrograms to synthetically generated tunes. The synthetic tunes are derived from ABC notation, which is a common symbolic representation for Irish music. Experimental results demonstrate that the wavelet-based method can accurately and efficiently identify recorded tunes. This research study also details the performance of the wavelet coherence model, highlighting its strengths over other methods of time-frequency decomposition. Additionally, we discuss and deploy the model on several applications beyond music, including in EEG signal analysis and financial time series forecasting.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。