Highly Efficient Audio Coding With Blind Spectral Recovery Based on Machine Learning

This letter proposes a new method for audio coding that utilizes blind spectral recovery to improve the coding efficiency without compromising performance. The proposed method transmits only a fraction of the spectral coefficients, thereby reducing the coding bit rate. Then, it recovers the remaining coefficients in the decoder using the transmitted coefficients as input. The proposed method is differentiated from conventional spectral recovery in that the coefficients to be recovered are interleaved with the transmitted coefficients to obtain the most data correlation. Further, it enhances the transmitted coefficients, which are degraded by quantization errors, to deliver better information to the recovery process. The spectral recovery is conducted recursively on a band basis such that information recovered in one band is used for the recovery in subsequent bands. An improved level correction for the recovered coefficients and a new sign coding are also developed. A subjective performance evaluation confirms that the proposed method at 40 kbps provides statistically equivalent sound quality to a state-of-the-art coding method at 48 kbps for speech and music categories.