标题： 基于小波的时间-频率指纹特征提取方法在传统爱尔兰音乐中的应用 显示英文标题

标题： Wavelet-Based Time-Frequency Fingerprinting for Feature Extraction of Traditional Irish Music

摘要： 这项工作提出了一种基于小波的方法，用于时间频率指纹识别，以实现时间序列特征提取，重点是从传统爱尔兰曲调的现场录音中进行音频识别。 通过采用连续小波变换来提取频谱特征，以及使用小波相干性分析将录制的音频频谱图与合成生成的曲调进行比较，解决了在时间序列数据中识别特征的挑战。 合成曲调来源于ABC记谱法，这是一种常见的爱尔兰音乐符号表示法。 实验结果表明，基于小波的方法可以准确且高效地识别录制的曲调。 本研究还详细介绍了小波相干模型的性能，突出了它在其他时间频率分解方法上的优势。 此外，我们讨论并部署该模型应用于音乐以外的多个领域，包括脑电图信号分析和金融时间序列预测。 显示英文摘要

摘要： 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.