Lossy Audio Compression Identification
Bongjun Kim, Zafar Rafii · 2018
We propose a system which can estimate from an audio recording that has previously undergone lossy compression the parameters used for the encoding, and therefore identify the corresponding lossy coding format. The system analyzes the audio signal and searches for the compression parameters and framing conditions which match those used for the encoding. In particular, we propose a new metric for measuring traces of compression which is robust to variations in the audio content and a new method for combining the estimates from multiple audio blocks which can refine the results. We evaluated this system with audio excerpts from songs and movies, compressed into various coding formats, using different bit rates, and captured digitally as well as through analog transfer. Results showed that our system can identify the correct format in almost all cases, even at high bit rates and with distorted audio, with an overall accuracy of 0.96.