Abstract
We present SoundPlot, an open-source framework for analyzing avian vocalizations through acoustic feature extraction, dimensionality reduction, and neural audio synthesis. The system transforms audio signals into a multi-dimensional acoustic feature space, enabling real-time visualization of temporal dynamics in 3D using web-based interactive graphics. Our framework implements a complete analysis-synthesis pipeline that extracts spectral features (centroid, bandwidth, contrast), pitch contours via probabilistic YIN (pYIN), and mel-frequency cepstral coefficients (MFCCs), mapping them to a unified timbre space for visualization. Audio reconstruction employs the Griffin-Lim phase estimation algorithm applied to mel spectrograms. The accompanying this http URL-based interface provides dual-viewport visualization comparing original and synthesized audio trajectories with independent playback controls. We demonstrate the framework's capabilities through comprehensive waveform analysis, spectrogram comparisons, and feature space evaluation using Principal Component Analysis (PCA). Quantitative evaluation shows mel spectrogram correlation scores exceeding 0.92, indicating high-fidelity preservation of perceptual acoustic structure. SoundPlot is released under the MIT License to facilitate research in bioacoustics, audio signal processing, and computational ethology.
Abstract (translated)
我们介绍了SoundPlot，这是一个开源框架，用于通过声学特征提取、降维和神经音频合成来分析鸟类鸣叫声。该系统将音频信号转换为一个多维的声学特性空间，从而能够使用基于网络的交互式图形实现实时3D时间动态可视化。我们的框架实现了一个完整的分析-综合管道，从频谱特征（中心频率、带宽、对比度）、通过概率YIN (pYIN) 提取音高轮廓以及梅尔频率倒谱系数（MFCCs），并将它们映射到一个统一的音色空间进行可视化。音频重构采用Griffin-Lim相位估计算法应用于梅尔光谱图。 该框架附带了一个基于Web的界面，提供了双视口可视化功能，可以比较原始和合成音频轨迹，并提供独立的播放控制。我们通过全面的波形分析、频谱图对比以及使用主成分分析（PCA）进行特征空间评估来展示该框架的能力。定量评价显示梅尔光谱相关得分超过0.92，表明感知声学结构的高度保真度保存。 SoundPlot根据MIT许可发布，以促进生物声学、音频信号处理和计算动物行为学的研究。
URL
https://arxiv.org/abs/2601.12752
