Many factors can affect the users’ quality of experience (QoE) in speech communication services. The impairment factors appear due to physical phenomena that occur in the transmission channel of wireless and wired networks. The monitoring of users’ QoE is important for service providers. In this context, a non-intrusive speech quality classifier based on the Tree Convolutional Neural Network (Tree-CNN) is proposed. The Tree-CNN is an adaptive network structure composed of hierarchical CNNs models, and its main advantage is to decrease the training time that is very relevant on speech quality assessment methods. In the training phase of the proposed classifier model, impaired speech signals caused by wired and wireless network degradation are used as input. Also, in the network scenario, different modulation schemes and channel degradation intensities, such as packet loss rate, signal-to-noise ratio, and maximum Doppler shift frequencies are implemented. Experimental results demonstrated that the proposed model achieves significant reduction of training time, reaching 25% of reduction in relation to another implementation based on DRBM. The accuracy reached by the Tree-CNN model is almost 95% for each quality class. Performance assessment results show that the proposed classifier based on the Tree-CNN overcomes both the current standardized algorithm described in ITU-T Rec. P.563 and the speech quality assessment method called ViSQOL.

/pdf/a-speech-quality-classifier-based-on-tree-cnn-algorithm-that-543y1kdyhd.pdf

A Speech Quality Classifier based on Tree-CNN Algorithm that Considers Network Degradations

Conversational applications such as telephony are mostly restricted to mono. With the emergence of VR/XR applications and new products with spatial audio, there is a need to extend traditional voice and audio codecs to enable immersive communication.The present work is motivated by recent activities in 3GPP standardization around the development of a new codec called immersive voice and audio services (IVAS). The IVAS codec will address a wide variety of use cases, e.g. immersive telephony, spatial audio conferencing, live content sharing. There are two main design goals for IVAS. One goal is the versatility of the codec in terms of input (scene-based, channel-based, object-based audio…) and output (mono, stereo, binaural, various multichannel loudspeaker setups). The second goal is to re-use as much as possible and extend the enhanced voice services (EVS) mono codec.In this work, we focus on the first-order ambisonic (FOA) format which is a good candidate for the internal representation in an immersive audio codec at low bit rates, due to the flexibility of the underlying sound field decomposition. We propose a new coding method, which can extend existing core codecs such as EVS. The proposed method consists in adaptively pre-processing ambisonic components prior to multi-mono coding by a core codec.The first part of this work investigates the basic multi-mono coding approach for FOA, which is for instance used in the Opus codec (in the so-called channel mapping family 2). In this approach ambisonic components are coded separately with different instances of the (mono) core codec. We present results of a subjective test (MUSHRA), which shows that this direct approach is not satisfactory for low-bitrate coding. The signal structure is degraded which produces many spatial artifacts (e.g. wrong panning, ghost sources...). In the second part of this work, we propose a new method to exploit the correlation of ambisonic components. The pre-processing (prior to multi-mono coding) operates in time-domain to allow maximum compatibility with many codecs, especially low bit-rate codecs such as EVS and Opus, and to minimize extra delay.The proposed method applies Principal Components Analysis (PCA) on a 20 ms frame basis. For each frame, eigenvectors are computed and the eigenvector matrix is defined as a 4D rotation matrix. For complex sound scenes (with many audio sources, sudden changes…) rotation parameters may change dramatically between consecutive frames and audio sources may go from one principal component to another, which may cause discontinuities or other artifacts. Solutions such as the interpolation of eigenvectors (after inter-frame realignment) are not optimal. In the proposed method, we ensure smooth transitions between inter-frame PCA rotations thanks to two complementary methods. The first one is a matching algorithm for eigenvectors between the current and the previous frame, which avoids signal inversion and permutation across frames. The second one is an interpolation of the 4D rotation matrices in quaternion domain. We use the Cayley factorization of 4D rotation matrices into a double quaternion for the current and previous frame and apply quaternion spherical linear interpolation (QSLERP) interpolation on a subframe basis. The interpolated rotation matrices are then applied to the ambisonic components and the decorrelated components are coded with a multi-mono coding approach.We present results of a subjective evaluation (MUSHRA) for the proposed method showing that the proposed method brings significant improvements over naive multi-mono method, especially in terms of spatial quality.

https://hal.archives-ouvertes.fr/hal-02275181/document

First-order ambisonic coding with quaternion-based interpolation of PCA rotation matrices

Encoding/decoding an immersive voice and audio services (IVAS) bitstream comprises: encoding/decoding a coding mode indicator in a common header (CH) section of an IVAS bitstream, encoding/decoding a mode header or tool header in the tool header (TH) section of the bitstream, the TH section following the CH section, encoding/decoding a metadata payload in a metadata payload (MDP) section of the bitstream, the MDP section following the CH section, encoding/decoding an enhanced voice services (EVS) payload in an EVS payload (EP) section of the bitstream, the EP section following the CH section, and on the encoder side, storing or streaming the encoded bitstream, and on the decoder side, controlling an audio decoder based on the coding mode, the tool header, the EVS payload, and the metadata payload or storing a representation of same.

Encoding and decoding ivas bitstreams

The present document describes a method (700) for encoding a multi-channel input signal (201). The method (700) comprises determining (701) a plurality of downmix channel signals (203) from the multi-channel input signal (201) and performing (702) energy compaction of the plurality of downmix channel signals (203) to provide a plurality of compacted channel signals (404). Furthermore, the method (700) comprises determining (703) joint coding metadata (205) based on the plurality of compacted channel signals (404) and based on the multi-channel input signal (201), wherein the joint coding metadata (205) is such that it allows upmixing of the plurality of compacted channel signals (404) to an approximation of the multi-channel input signal (201). In addition, the method (700) comprises encoding (704) the plurality of compacted channel signals (404) and the joint coding metadata (205).

Methods and devices for encoding and/or decoding immersive audio signals

Embodiments are disclosed for bitrate distribution in immersive voice and audio services. In an embodiment, a method of encoding an IVAS bitstream comprises: receiving an input audio signal; downmixing the input audio signal into one or more downmix channels and spatial metadata; reading a set of one or more bitrates for the downmix channels and a set of quantization levels for the spatial metadata from a bitrate distribution control table; determining a combination of the one or more bitrates for the downmix channels; determining a metadata quantization level from the set of metadata quantization levels using a bitrate distribution process; quantizing and coding the spatial metadata using the metadata quantization level; generating, using the combination of one or more bitrates, a downmix bitstream for the one or more downmix channels; combining the downmix bitstream, the quantized and coded spatial metadata and the set of quantization levels into the IVAS bitstream.

Bitrate distribution in immersive voice and audio services

Virtual Reality (VR) audio scenes may be composed of a very large number of audio elements, including dynamic audio objects, fixed audio channels and scene-based audio elements such as Higher Order Ambisonics (HOA). Potentially, the subjective listening experience may be replicated using a compact spatial format with a set number of dynamic objects and scene-based elements, retaining only the perceptual essence of the audio scene. The compact format would further enable a reduction in the complexity of subsequent compression and rendering. This paper investigates these hypotheses by exploring the use of a compact format that consists of up to four dynamic objects and nine HOA channels, with the Enhanced Voice Services (EVS) codec being applied to a 4-channel down-mix of the compact format.

Immersive Audio Coding for Virtual Reality Using a Metadata-assisted Extension of the 3GPP EVS Codec

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Immersive Audio Coding for Virtual Reality Using a Metadata-assisted Extension of the 3GPP EVS Codec