Stftcodec: High-fidelity Audio Compression Through Time-frequency Domain Representation
2025 Β· Tao Feng, Zhiyuan Zhao, Yifan Xie, et al.
Abstract
We present STFTCodec, a novel spectral-based neural audio codec that efficiently compresses audio using Short-Time Fourier Transform (STFT). Unlike waveform-based approaches that require large model capacity and substantial memory consumption, this method leverages STFT for compact spectral representation and introduces unwrapped phase derivatives as auxiliary features. Our architecture employs parallel magnitude and phase processing branches enhanced by advanced feature extraction mechanisms. By relaxing strict phase reconstruction constraints while maintaining phase-aware processing, we achieve superior perceptual quality. Experimental results demonstrate that STFTCodec outperforms both waveform-based and spectral-based approaches across multiple bitrates, while offering unique flexibility in compression ratio adjustment through STFT parameter modification without architectural changes.
Authors
(none)
Tags
Stats
Related papers
- Apcodec+: A Spectrum-coding-based High-fidelity And High-compression-rate Neural Audio Codec With Staged Training Paradigm (2024)0.00
- Mdctcodec: A Lightweight Mdct-based Neural Audio Codec Towards High Sampling Rate And Low Bitrate Scenarios (2024)8.09
- Spatialcodec: Neural Spatial Speech Coding (2023)3.69
- Spectral Codecs: Improving Non-autoregressive Speech Synthesis With Spectrogram-based Audio Codecs (2024)0.00
- Freecodec: A Disentangled Neural Speech Codec With Fewer Tokens (2024)4.52
- Funcodec: A Fundamental, Reproducible And Integrable Open-source Toolkit For Neural Speech Codec (2023)17.47
- Optimizing Neural Speech Codec For Low-bitrate Compression Via Multi-scale Encoding (2024)0.00
- Codecslime: Temporal Redundancy Compression Of Neural Speech Codec Via Dynamic Frame Rate (2025)0.00