An overview of the coding standard MPEG-4 audio amendments 1 and 2: HE-AAC, SSC, and HE-AAC v2
EURASIP Journal on Audio, Speech, and Music Processing
Error Resilient Speech Coding Using Sub-band Hilbert Envelopes
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
Autoregressive models of amplitude modulations in audio compression
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Signal Processing
Wide-band audio coding based on frequency-domain linear prediction
EURASIP Journal on Audio, Speech, and Music Processing - Special issue on scalable audio-content analysis
A ramp cosine cepstrum model for the parameter estimation of autoregressive systems at low SNR
EURASIP Journal on Advances in Signal Processing
Structured Sparsity Models for Reverberant Speech Separation
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
| Hi-index | 35.69 |
Autoregressive (AR) models are commonly obtained from the linear autocorrelation of a discrete-time signal to obtain an all-pole estimate of the signal's power spectrum. We are concerned with the dual, frequency-domain problem. We derive the relationship between the discrete-frequency linear autocorrelation of a spectrum and the temporal envelope of a signal. In particular, we focus on the real spectrum obtained by a type-I odd-length discrete cosine transform (DCT-Io) which leads to the all-pole envelope of the corresponding symmetric squared Hilbert temporal envelope. A compact linear algebra notation for the familiar concepts of AR modeling clearly reveals the dual symmetries between modeling in time and frequency domains. By using AR models in both domains in cascade, we can jointly estimate the temporal and spectral envelopes of a signal. We model the temporal envelope of the residual of regular AR modeling to efficiently capture signal structure in the most appropriate domain.