Power Spectrum Analysis in Audio Fingerprint Systems

In this article, we conduct an in-depth investigation of power spectrum analysis in audio fingerprint systems. By analyzing the frequency spectrum of audio signals, we can better understand their properties and characteristics, enabling more accurate and stable audio recognition. Specifically, we examine how power spectrum analysis can be utilized to extract key features for audio fingerprints and how these features can be compared against known fingerprints in a database for matching purposes. The implementation typically involves computing the Fast Fourier Transform (FFT) on audio frames, followed by power spectral density calculation using methods like periodogram or Welch's method. Feature extraction algorithms may then process spectral peaks, energy distributions, or spectral centroids to create compact fingerprint representations. Additionally, we introduce commonly used power spectrum analysis techniques and tools, along with their practical applications in audio fingerprint systems. Common implementations might involve Python libraries such as NumPy for FFT computations and SciPy for spectral analysis, while database matching often employs efficient similarity search algorithms like locality-sensitive hashing (LSH). In summary, this article aims to provide readers with a deeper understanding of power spectrum analysis in audio fingerprint systems and offer valuable references for related research and development projects.