This paper presents a physically inspired source-filter model for rolling sound synthesis. The model, which is suitable for real-time implementation, is based on qualitative and quantitative observations obtained from a physics-based model described in the literature. In the first part of the paper, the physics-based model is presented, followed by a perceptual experiment, whose aim is to identify the perceptually relevant information characterizing the rolling interaction. On the basis of this experiment, we hypothesize that the particular pattern of the interaction force is responsible for the perception of a rolling object. A complete analysis-synthesis scheme of this interaction force is then provided, along with a description of the calibration of the proposed source-filter sound synthesis process. Finally, a mapping strategy for intuitive control of the proposed synthesis process (i.e. size and velocity of the rolling object and roughness of the surface) is proposed and validated by a listening test.

Sinusoidal modeling is one of the most popular techniques for low bitrate audio coding. Usually, the sinusoidal parameters (amplitude, pulsation and phase of each sinusoidal component) are kept constant within a time segment. An alternative model, the so-called Exponentially-Damped Sinusoidal (EDS) model, includes an additional damping parameter for each sinusoidal component to better represent the signal characteristics. It was however never shown that the EDS model could be efficient for perceptual audio coding. To that aim, we propose in this paper an efficient analysis/synthesis framework with dynamic time-segmentation on transients and psychoacoustic modeling, and an asymptotically optimal entropy-constrained quantization method for the four sinusoid parameters (e.g., including damping). We then apply this coding technique to real audio excerpts for a given entropy target corresponding to a low bitrate (20 kbits/s), and compare this method with a classical sinusoidal coding scheme using a constant-amplitude sinusoidal model and the perceptually weighted Matching Pursuit algorithm. Subjective listening tests show that the EDS model is more efficient on audio samples with fast transient content, and similar to the classical model for more stationary audio samples.

Sinusoidal modeling is traditionally one of the most popular techniques for low bitrate audio coding. Usually, the sinusoidal parameters are kept constant within a time segment but the exponentially damped sinusoidal (EDS) model is also an efficient alternative. However, the inclusion of an additional damping parameter calls for a specific quantization scheme. In this paper, we propose an asymptotically optimal entropy-constrained quantization method for amplitude, phase and damping parameters. We show that this scheme is nearly optimal in terms of rate-distortion trade-off. We also show that damping consumes the smallest part of the total entropy of quantization indexes, which suggests that the EDS model is truly efficient for audio coding.

This paper presents a bandwidth-efficient method for improved decoding of critical data generated by the MPEG-4 AAC audio coder when encoded bitstreams are transmitted over noisy channels. The critical data of each encoded frame is estimated using the redundancy due to the correlation between successive frame headers and using an optional CRC as an error-correcting code. Simulation results for an AWGN channel show a substantial link budget improvement (more than 5 dB) compared to a classical hard-decoding method. Once critical data are efficiently estimated, the work of previously proposed joint source-channel decoding techniques to recover the remaining parts of the MPEG4 AAC frames is significantly facilitated.

In this paper, a new model-based algorithm for optimizing the MPEG-advanced audio coder (AAC) in MS-stereo mode is presented. This algorithm is an extension to stereo signals of prior work on a statistical model of quantization noise. Traditionally, MS-stereo coding approaches replace the left (l) and right (R) channels by the middle (M) and sides (S) channels, each channel being independently processed, almost like a monophonic signal. In contrast, our method proposes a global approach for coding both channels in the same process. A model for the quantization error allows us to tune the quantizers on channels M and S with respect to a distortion constraint on the reconstructed channels L and R as they will appear in the decoder. This approach leads to a more efficient perceptual noise-shaping and avoids using complex psychoacoustic models built on the M and S channels. Furthermore, it provides a straightforward scheme to choose between LR and MS modes in each subband for each frame. Subjective listening tests prove that the coding efficiency at a medium bitrate (96 kbits/s for both channels) is significantly better with our algorithm than with the standard algorithm, without increase of complexity.

A new method for analyzing audio signals in the context of musical transcription is described. It consists of a frame-based expansion of the signal over a multi-scale time-frequency dictionary with a set of logarithmic discrete frequencies. This method, based on the matching pursuit algorithm, provides the same frequency resolution as a constant-Q filter-bank, but with a better time resolution, especially in low frequencies, and an efficient noise rejection

In this paper, an improvement of the quantization optimization algorithm for the MPEG Advanced Audio Coder (AAC) is presented. This algorithm, given a bit-rate constraint, minimizes the perceived distortion generated by the signal compression. The distortion can be related to the quantization error level over frequency subbands through an auditory model. Thus, optimizing the quantization requires knowledge of the rate-distortion function for each subband. When this function can be modeled in a simple way, the algorithm can take a one-loop recursive structure. However, in the MPEG AAC, the rate-distortion function is hard to characterize, since AAC makes use of nonlinear quantizers and variable length entropy coders. As a result, the standard algorithm makes use of two nested loops with a local decoder, in order to measure the error level rather than predicting its value. We first describe a partial subband modeling of the rate-distortion function of interest in the MPEG AAC. Then, using a statistical approach, we find a relationship between the error level and the so-called quantization "scale-factor" and propose a new algorithm that is basically similar to a classical one loop "bit allocation" process. Finally, we describe the complete algorithm and show that it is more efficient than the standard one

In an MPEG Advanced Audio Coder (AAC), for each frequency subband, the quantization parameter called scale factor controls the quantization noise and the bit rate. Tuning this coder would require a characterization of the rate-distortion function per subband, which seems to be available only in a parametric manner. In this paper, we show that an accurate model for the quantization noise, depending on the scale factor value, can lead to a simple expression of the relation between the scale factor and the error power per subband. In other terms, we are now able to obtain an explicit writing of the distortion-rate function. In a second time, we apply these results to the coding algorithm in case of a fixed output bit rate and propose a new coding algorithm. Unlike the one described in the informative annex of the MPEG-2 standard, it makes use of a single loop and happens to be faster and more flexible towards psychoacoustic criteria.

In a MPEG-4 analytic audio scene, spatialization filters can be used after an audio coder. When properly tuned, a MPEG advanced audio coder (AAC), generates an inaudible distortion if a sufficient output bit-rate is available, and, more important in the context of our study, as long as the listener has direct access to the unprocessed output of the decoder. We show that using a spatialization filter after the decoder, as described in the MPEG-4 standard, may damage the perceived quality. We propose solutions and present a modified encoder working with a hearing model that includes all audio processing (including spatialization), which allows bit-rate reduction without quality damage.