如何通过Spotify API获取歌曲的MFCC？及相关方法咨询

Hey there! Let's walk through your questions about extracting MFCCs (Mel-Frequency Cepstral Coefficients) from Spotify tracks, plus clarify that mysterious tmfccrack field.

First, your existing approaches #

1. Extracting MFCCs from Spotify's Audio Analysis API #

Unfortunately, Spotify's https://api.spotify.com/v1/audio-analysis/{id} endpoint doesn't return raw or usable MFCC data. And to answer your direct question: the tmfccrack field has no connection to MFCCs. It's part of Spotify's internal audio fingerprinting system—essentially a proprietary parameter for their own matching algorithms, so you can't use it to derive MFCC features at all.

2. Calculating MFCCs from audio via third-party libraries #

This is a much more viable path, but there's a catch: Spotify's API doesn't let you download raw audio files. To get the audio data you need, you have a couple of compliant options:

• Use Spotify's official SDKs (like the Web Playback SDK, iOS SDK, or Android SDK) to stream the track, then capture the audio in real-time (make sure to stick to Spotify's developer terms—you can't store or redistribute the audio, only analyze it on the fly). Once you have the audio stream, use libraries like librosa (Python), Essentia (cross-platform), or TensorFlow Audio to compute MFCCs directly.
• For non-commercial research, you could use legitimate audio capture tools (again, strictly adhering to copyright and Spotify's terms of service) to save the audio locally, then process it with the same libraries. But the SDK route is always the safer, compliant choice.

Other feasible methods #

• Third-party music data APIs: Some specialized music data platforms offer pre-computed MFCC features for tracks (just double-check their data licensing and accuracy before relying on them).
• Leverage Spotify's built-in audio features: While not MFCCs, the /v1/audio-features/{id} endpoint returns a set of processed features (tempo, loudness, speechiness, valence, etc.) that can be used to train genre classification models. If your algorithm can adapt, this might be a faster alternative—though if you specifically need MFCCs, the audio capture + library method is still your best bet.

Quick note on your genre recognition goal #

You mentioned using artist genres as an indirect label, which is a solid baseline. But for MFCC-based model training, the real value comes from pairing your computed MFCCs with verified track-level genre labels (you might need to combine Spotify's artist/album genre data with crowdsourced labels for better accuracy).

内容的提问来源于stack exchange，提问作者Karan