Autoregressive Models of Amplitude Modulations in Audio Compression

Abstract

We present a scalable medium bit-rate wide-band audio coding technique based on frequency domain linear prediction (FDLP). FDLP is an efficient method for representing the long-term amplitude modulations of speech/audio signals using autoregressive models. For the proposed audio codec, relatively long temporal segments (1000 ms) of the input audio signal are decomposed into a set of critically sampled sub-bands using a quadrature mirror filter (QMF) bank. The technique of FDLP is applied on each sub-band to model the sub-band temporal envelopes. The residual of the linear prediction, which represents the frequency modulations in the sub-band signal [1], are encoded and transmitted along with the envelope parameters. These steps are reversed at the decoder to reconstruct the signal. The proposed codec utilizes a simple signal independent non-adaptive compression mechanism for a wide class of speech and audio signals. The subjective and objective quality evaluations show that the reconstruction signal quality for the proposed FDLP codec compares well with the state-of-the-art audio codecs in the 32-64 kbps range. Index TermsSpeech and Audio coding, Modulation Spectrum, Frequency Domain Linear Prediction (FDLP), Objective and subjective evaluation of audio quality.

Temporal Envelope Fit of Transient Audio Signals

“…Since it is not possible to obtain a closed-form solution, we propose a fixed-point iterative technique to solve for . The th update equation of the iteration is given as (5) with the initialization . The procedure is terminated at iteration where .…”

“…Parametric coding techniques based on Meixner transients have been applied in MPEG-4 extensions, based on the decomposition of audio signals into transients, sinusoids, and noise components [2]. Recently, frequency-domain linear prediction approaches have been proposed to model the temporal envelope [3]- [5]. The envelope can be represented either nonparametrically and compressed in the transform domain, or parametrically to enable direct coding.…”

Spectral-envelope–group-delay models for transients

“…Another approach to estimate the minimum-phase component is to perform linear prediction in the spectral domain, 12 which is the dual of the classical time-domain linear prediction and has found good use in many speech and audio processing applications. [13][14][15] B. Group delay models and applications…”

Fast Dynamic Speech Recognition via Discrete Tchebichef Transform

“…Autoregressive model describes the output of filtering a temporally uncorrelated excitation sequence through all pole estimate of the signal. Autoregressive models have been used in speech recognition for representing the envelope of the power spectrum of the signal by performing the operation of linear prediction [19]. Autoregressive model is used to determine the characteristics of the vocal and to evaluate the formants.…”