The transmission of stereo audio in voice calls helps to improve immersion and user experience. However stereo coding has primarily been studied for broadcast or streaming applications. To extend the capabilities of 3GPP EVS, a hybrid stereo coding scheme combining predictive and parametric coding is proposed to meet the requirements of real-time communications. After the stereo channels are time-aligned, a sum-difference (M/S) decomposition is performed. The prediction of the side signal by the mid signal is used to capture the other spatial cues. The prediction residue can be parametrically modeled by a stereo filling or, at higher bitrates and for low frequencies, discretely coded. Listening test results confirm the stereo coding scheme’s great flexibility and robustness under different recording conditions, as well as the minimal quality impact of the low-bitrate and low-delay constraints.

In real-time teleconferences over mobile networks, cross-talk can significantly impact the performance of parametric stereo codecs, particularly at low bitrates. When multiple speakers overlap, stereo quality can be improved by independently encoding the left and right channels, especially when inter-channel correlation is low. This approach can be implemented using the dual-mono EVS coder, for example. The recently standardized 3GPP IVAS codec incorporates both a parametric stereo model and an independent left/right stereo model based on the dual-mono EVS codec. To address cross-talk, the 3GPP IVAS codec features a cross-talk classifier that uses a multivariate statistical model based on GCC-PHAT and other spatial cues, allowing for seamless switching between the two stereo models. Listening tests show that the IVAS stereo codec enhances performance in single-talker scenarios while maintaining quality in cross-talk segments.

Speech coders operating in time domain can be extended with a frequency domain mode to improve encoding of music, even though this is challenging at low delay. In such a scenario, the short analysis window limits the benefit of the transform coder, while a delayless switch between the two coders constrains the system further. The paper presents an LPC and MDCT-based audio coder part of the new 3GPP codec for Enhanced Voice Services, which aims to solve the issues. Several advanced coding tools are introduced to alleviate the constraints: transient handling is improved, harmonic structures are better preserved, and the modeling of the zero-quantized frequencies is enhanced. Test results show that the obtained low-delay switched coder brings a clear improvement over a speech coder and is competitive even in comparison to audio coders with higher delay.

Discontinuous Transmission (DTX) is an efficient way to drastically reduce the transmission rate of a communication codec in the absence of voice input. In this mode, most frames that are determined to consist of background noise only are dropped from transmission and replaced by some Comfort Noise Generation (CNG) in the decoder. In this paper, we propose a novel CNG approach combining information gained about the actual background noise at both encoder and decoder side. It is able to better reproduce background noise types showing a pronounced spectral tilt, which is difficult for traditional schemes based on a linear prediction model. The proposed technique operates in the frequency domain. It is part of the Enhanced Voice Services (EVS) codec, where it is known as FD-CNG. Listening tests show the superior quality of FD-CNG over existing approaches for certain background noise such as car noise.

Several state-of-the-art switched audio codecs employ the closed-loop mode decision to select the best coding mode at every frame. The closed-loop mode selection is known to have good performance but also high complexity. The new approach we propose in this paper is a low-complexity version of the closed-loop approach, based on similar decisions which compute the coding distortion of each mode and select the one with the lowest distortion. Our approach differs mainly in the way the coding distortions are calculated. We are able to notably reduce the complexity by only estimating the distortions without encoding and decoding the input for each mode. The new approach was implemented in the EVS codec standard and evaluated both objectively and subjectively. Compared to the closed-loop approach, it yields similar performance and lower complexity.

Current scalable audio coders typically optimize performance at a particular layer without regard to impact on other layers, and are thus unable to provide a performance trade-off between different layers. In the particular case of MPEG Scalable Advanced Audio Coding (S-AAC) and Scalable-to-Lossless (SLS) coding, the base-layer is optimized first followed by successive optimization of higher layers, which ensures optimality of the base-layer but results in a scalability penalty that progressively increases with the enhancement layer index. The ability to trade-off performance between different layers enables alignment to the real world requirement for audio quality commensurate with the bandwidth afforded by a user. This work provides the means to better control the performance tradeoffs, and the distribution of the scalability penalty, between the base and enhancement layers. Specifically, it proposes an efficient joint optimization algorithm that selects the encoding parameters for each layer while accounting for the rate-distortion costs in all layers. The efficacy of the technique is demonstrated in the two distinct settings of S-AAC, and SLS High Definition Advanced Audio Coding. Objective and subjective tests provide evidence for substantial gains, and represent a significant step toward bridging the gap with the non-scalable coder.

MPEG-4 High-Definition Advanced Audio Coding (HD-AAC) enables scalable-to-lossless (SLS) audio coding with an Advanced Audio Coding (AAC) base layer, and fine-grained enhancements based on the MPEG SLS standard. While the AAC core offers better perceptual quality at lossy bit-rates, its inclusion has been observed to compromise the ultimate lossless compression performance as compared to the SLS `non-core' (i.e., without an AAC base layer) codec. In contrast, the latter provides excellent lossless compression but with significantly degraded audio quality at low bit-rates. We propose a trellis-based approach to directly optimize the trade-off between the quality of the AAC core and the lossless compression performance of SLS. Simulations to test the effectiveness of the approach demonstrate the capability to adjust the trade-off to match application specific needs. Moreover, such optimization can in fact achieve an AAC core of superior perceptual quality while maintaining state-of-the-art (and surprisingly sometimes even better) lossless compression, all this in compliance with the HD-AAC standard.

Indexing audio signals directly in the transform domain can potentially save a significant amount of computation when working on a large database of signals stored in a lossy compression format, without having to fully decode the signals. Here, we show that the representations used in standard transform-based audio codecs (e.g., MDCT for AAC, or hybrid PQF/MDCT for MP3) have a sufficient time resolution for some rhythmic features, but a poor frequency resolution, which prevents their use in tonality-related applications. Alternatively, a recently developed audio codec based on a sparse multi-scale MDCT transform has a good resolution both for time- and frequency-domain features. We show that this new audio codec allows efficient transform-domain audio indexing for three different applications, namely beat tracking, chord recognition, and musical genre classification. We compare results obtained with this new audio codec and the two standard MP3 and AAC codecs, in terms of performance and computation time.

This paper investigates the use of sparse overcomplete decompositions for audio coding. Audio signals are decomposed over a redundant union of modified discrete cosine transform (MDCT) bases having eight different scales. This approach produces a sparser decomposition than the traditional MDCT-based orthogonal transform and allows better coding efficiency at low bitrates. Contrary to state-of-the-art low bitrate coders, which are based on pure parametric or hybrid representations, our approach is able to provide transparency. Moreover, we use a bitplane encoding approach, which provides a fine-grain scalable coder that can seamlessly operate from very low bitrates up to transparency. Objective evaluation, as well as listening tests, show that the performance of our coder is significantly better than a state-of-the-art transform coder at very low bitrates and has similar performance at high bitrates. We provide a link to test soundfiles and source code to allow better evaluation and reproducibility of the results.

We propose a novel system able to modify the rhythm of a given drum loop, known as the original, to match the rhythmic pattern of a second loop, known as the model. Our approach is fully automated, thus eliminating the need for MIDI sequencing. The presented methodology combines standard and state-of-the-art techniques for the segmentation and classification of drum sounds, the matching of drum sequences, and the transformation of the original loop. We discuss the advantages and disadvantages of the proposed approach and provide links to examples for the qualitative evaluation of the system's output

Embedded polar quantization can be useful for progressive transmission of circularly symmetric data, e.g., for fine-grain scalable coding of parametric audio. Sets of constrained-resolution embedded quantizers are built recursively by successive refinement processes, that are detailed for strict polar quantization and unrestricted polar quantization. The quadratic error minimization problem is solved using equations similar to those of Max, and the refinement algorithm can, in the unrestricted case, be simplified using a high-rate approximation. For Gaussian data, comparisons with reference non-embedded quantizers show that the embedding property comes at an often negligible cost in terms of rate-distortion performance.

This paper addresses the issue of drum sound classification in the context of automatic rhythm modification of drum loops. The proposed method segments the signal using an onset detection algorithm, characterises segmented sounds using a spectral feature set, and classifies them using k-means clustering. We propose a simple taxonomy for the grouping of different instrumental sounds under a few utilitarian labels. Results demonstrate the adequacy of our proposed taxonomy while showing that our classification approach outperforms commonly-used supervised learning techniques

We propose in this paper a two-stage lossless audio coder for stereo signals. The first stage is based on a hybrid approach that uses either a stereo linear prediction or a multilayer perceptron-based (MLP) nonlinear prediction. The second stage is based on two cascaded normalized least mean square (NLMS) filters that remove the remaining redundancy in the residuals of the stereo prediction. The obtained compression ratios are equal or superior to the best state-of-the-art coders