Superwideband Bandwidth Extension Using Normalized MDCT Coefficients for Scalable Speech and Audio Coding

A bandwidth extension (BWE) algorithm from wideband to superwideband (SWB) is proposed for a scalable speech/audio codec that uses modified discrete cosine transform (MDCT) coefficients as spectral parameters. The superwideband is first split into several subbands that are represented as gain parameters and normalized MDCT coefficients in the proposed BWE algorithm. We then estimate normalized MDCT coefficients of the wideband to be fetched for the superwideband and quantize the fetch indices. After that, we quantize gain parameters by using relative ratios between adjacent subbands. The proposed BWE algorithm is embedded into a standard superwideband codec, the SWB extension of G.729.1 Annex E, and its bitrate and quality are compared with those of the BWE algorithm already employed in the standard superwideband codec. It is shown from the comparison that the proposed BWE algorithm relatively reduces the bitrate by around 19% with better quality, compared to the BWE algorithm in the SWB extension of G.729.1 Annex E

Reviews on Technology and Standard of Spatial Audio Coding

Market  demands  on a more impressive entertainment media have motivated for delivery of three dimensional  (3D) audio content to  home consumers  through Ultra  High  Definition  TV  (UHDTV), the next generation of TV broadcasting, where spatial  audio  coding plays  fundamental role. This paper reviews fundamental concept on spatial audio coding which includes technology, standard, and application. Basic principle of object-based audio reproduction system  will also be elaborated, compared  to  the  traditional channel-based system, to provide good understanding on this popular interactive audio reproduction system which gives end users flexibility to render  their  own preferred  audio composition.Keywords : spatial audio, audio coding, multi-channel audio signals, MPEG standard, object-based audi

Scalable Speech Coding for IP Networks

The emergence of Voice over Internet Protocol (VoIP) has posed new challenges to the development of speech codecs. The key issue of transporting real-time voice packet over IP networks is the lack of guarantee for reasonable speech quality due to packet delay or loss. Most of the widely used narrowband codecs depend on the Code Excited Linear Prediction (CELP) coding technique. The CELP technique utilizes the long-term prediction across the frame boundaries and therefore causes error propagation in the case of packet loss and need to transmit redundant information in order to mitigate the problem. The internet Low Bit-rate Codec (iLBC) employs the frame-independent coding and therefore inherently possesses high robustness to packet loss. However, the original iLBC lacks in some of the key features of speech codecs for IP networks: Rate flexibility, Scalability, and Wideband support. This dissertation presents novel scalable narrowband and wideband speech codecs for IP networks using the frame independent coding scheme based on the iLBC. The rate flexibility is added to the iLBC by employing the discrete cosine transform (DCT) and iii the scalable algebraic vector quantization (AVQ) and by allocating different number of bits to the AVQ. The bit-rate scalability is obtained by adding the enhancement layer to the core layer of the multi-rate iLBC. The enhancement layer encodes the weighted iLBC coding error in the modified DCT (MDCT) domain. The proposed wideband codec employs the bandwidth extension technique to extend the capabilities of existing narrowband codecs to provide wideband coding functionality. The wavelet transform is also used to further enhance the performance of the proposed codec. The performance evaluation results show that the proposed codec provides high robustness to packet loss and achieves equivalent or higher speech quality than state-of-the-art codecs under the clean channel condition

Étude de transformées temps-fréquence pour le codage audio faible retard en haute qualité

In recent years there has been a phenomenal increase in the number of products and applications which make use of audio coding formats. Amongthe most successful audio coding schemes, the MPEG-1 Layer III (mp3), the MPEG-2 Advanced Audio Coding (AAC) or its evolution MPEG-4High Efficiency-Advanced Audio Coding (HE-AAC) can be cited. More recently, perceptual audio coding has been adapted to achieve codingat low-delay such to become suitable for conversational applications. Traditionally, the use of filter bank such as the Modified Discrete CosineTransform (MDCT) is a central component of perceptual audio coding and its adaptation to low delay audio coding has become an important researchtopic. Low delay transforms have been developed in order to retain the performance of standard audio coding while reducing dramatically the associated algorithmic delay.This work presents some elements allowing to better accommodate the delay reduction constraint. Among the contributions, a low delay blockswitching tool which allows the direct transition between long transform and short transform without the insertion of transition window. The sameprinciple has been extended to define new perfect reconstruction conditions for the MDCT with relaxed constraints compared to the original definition.As a consequence, a seamless reconstruction method has been derived to increase the flexibility of transform coding schemes with the possibility toselect a transform for a frame independently from its neighbouring frames. Finally, based on this new approach, a new low delay window design procedure has been derived to obtain an analytic definition for a new family of transforms, permitting high quality with a substantial coding delay reduction. The performance of the proposed transforms has been thoroughly evaluated, an evaluation framework involving an objective measurement of the optimal transform sequence is proposed. It confirms the relevance of the proposed transforms used for audio coding. In addition, the new approaches have been successfully applied to the recent standardisation work items, such as the low delay audio coding developed at MPEG (LD-AAC and ELD-AAC) and they have been evaluated with numerous subjective testing, showing a significant improvement of the quality for transient signals. The new low delay window design has been adopted in G.718, a scalable speech and audio codec standardized in ITU-T and has demonstrated its benefit in terms of delay reduction while maintaining the audio quality of a traditional MDCT.Codage audio à faible retard à l'aide de la définition de nouvelles fenêtres pour la transformée MDCT et l'introduction d'un nouveau schéma de commutation de fenêtre

Secure VoIP Performance Measurement

This project presents a mechanism for instrumentation of secure VoIP calls. The experiments were run under different network conditions and security systems. VoIP services such as Google Talk, Express Talk and Skype were under test. The project allowed analysis of the voice quality of the VoIP services based on the Mean Opinion Score (MOS) values generated by Perceptual valuation of Speech Quality (PESQ). The quality of the audio streams produced were subjected to end-to-end delay, jitter, packet loss and extra processing in the networking hardware and end devices due to Internetworking Layer security or Transport Layer security implementations. The MOS values were mapped to Perceptual Evaluation of Speech Quality for wideband (PESQ-WB) scores. From these PESQ-WB scores, the graphs of the mean of 10 runs and box and whisker plots for each parameter were drawn. Analysis on the graphs was performed in order to deduce the quality of each VoIP service. The E-model was used to predict the network readiness and Common vulnerability Scoring System (CVSS) was used to predict the network vulnerabilities. The project also provided the mechanism to measure the throughput for each test case. The overall performance of each VoIP service was determined by PESQ-WB scores, CVSS scores and the throughput. The experiment demonstrated the relationship among VoIP performance, VoIP security and VoIP service type. The experiment also suggested that, when compared to an unsecure IPIP tunnel, Internetworking Layer security like IPSec ESP or Transport Layer security like OpenVPN TLS would improve a VoIP security by reducing the vulnerabilities of the media part of the VoIP signal. Morever, adding a security layer has little impact on the VoIP voice quality

Extensão artificial de largura de banda para sinais de fala em telefonia usando classificação fonética

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2016.Este trabalho de pesquisa apresenta uma nova estratégia para implementar sistemas de extensão artificial de largura de banda (artificial bandwidth extension- ABWE) para sinais de fala aplicados à rede pública (convencional) de telefonia (public switched telephone network - PSTN). Especificamente, aqui é proposta uma estratégia baseada em classificação fonética visando representar satisfatoriamente segmentos de fala com energia concentrada em altas frequências, superando outros resultados apresentados na literatura. Para tal,técnicas de seleção de atributos aplicadas a sinais de fala de banda limitada são investigadas, aprimorando a classificação em grupos fonéticos abrangentes (broad group phonetic - BGP) com ênfase na discriminação de fonemas pertencentes ao grupo fonético fricativo. Adicionalmente, neste trabalho é discutida a integração do sistema de ABWE proposto em sistemas de reconhecimento automático de fala (automatic speech recognition - ASR) para o português brasileiro aplicados à PSTN. Particularmente, visando o aprimoramento de ASR em PSTN, as etapas de extração de atributos do sinal da fala e a etapa de construção do modelo acústico são desenvolvidas baseadas em sinais sintéticos de banda larga (wideband - WB) estimados a partir do realce de sinais de banda estreita (narrowband - NB) usando ABWE. Os resultados obtidos apresentam realce na qualidade subjetiva dos sinais de fala reconstruídos e ganho no desempenho do ASR, confirmando a eficácia das estratégias propostas neste trabalho de pesquisa.Abstract: This research work presents a new strategy for implementing artificial bandwidth extension (ABWE) systems for speech signals applied to the public switched telephone network (PSTN). Specifically, a strategy based on phonetic classification is proposed here aiming to represent speech segments with concentrated energy at high frequencies, out performing other approaches from the open literature. In this context, feature selection techniques applied to limited band width speech signals are investigated, improving thebroad group phonetic (BGP) classification with an emphasis on discriminationof phonemes belonging to the fricative phonetic group. In addition, the integration of the proposed ABWE approach in automatic speech recognition(ASR) systems for Brazilian Portuguese applied to the PSTN is also discussed. Particularly, in order to improve PSTN ASR systems, synthetically estimated wide band (WB) signals, from the narrow band (NB) enhancement by ABWE, are used to obtain more discriminating attributes of speech signals as well as for achieving better performance of acoustic models (AM). Theobtained results show an enhancement in the quality of reconstructed speech signals with very good performance in ASR systems, confirming the effectivenessof the proposed strategies in this research work

Apprentissage automatique pour le codage cognitif de la parole

Depuis les années 80, les codecs vocaux reposent sur des stratégies de codage à court terme qui fonctionnent au niveau de la sous-trame ou de la trame (généralement 5 à 20 ms). Les chercheurs ont essentiellement ajusté et combiné un nombre limité de technologies disponibles (transformation, prédiction linéaire, quantification) et de stratégies (suivi de forme d'onde, mise en forme du bruit) pour construire des architectures de codage de plus en plus complexes. Dans cette thèse, plutôt que de s'appuyer sur des stratégies de codage à court terme, nous développons un cadre alternatif pour la compression de la parole en codant les attributs de la parole qui sont des caractéristiques perceptuellement importantes des signaux vocaux. Afin d'atteindre cet objectif, nous résolvons trois problèmes de complexité croissante, à savoir la classification, la prédiction et l'apprentissage des représentations. La classification est un élément courant dans les conceptions de codecs modernes. Dans un premier temps, nous concevons un classifieur pour identifier les émotions, qui sont parmi les attributs à long terme les plus complexes de la parole. Dans une deuxième étape, nous concevons un prédicteur d'échantillon de parole, qui est un autre élément commun dans les conceptions de codecs modernes, pour mettre en évidence les avantages du traitement du signal de parole à long terme et non linéaire. Ensuite, nous explorons les variables latentes, un espace de représentations de la parole, pour coder les attributs de la parole à court et à long terme. Enfin, nous proposons un réseau décodeur pour synthétiser les signaux de parole à partir de ces représentations, ce qui constitue notre dernière étape vers la construction d'une méthode complète de compression de la parole basée sur l'apprentissage automatique de bout en bout. Bien que chaque étape de développement proposée dans cette thèse puisse faire partie d'un codec à elle seule, chaque étape fournit également des informations et une base pour la prochaine étape de développement jusqu'à ce qu'un codec entièrement basé sur l'apprentissage automatique soit atteint. Les deux premières étapes, la classification et la prédiction, fournissent de nouveaux outils qui pourraient remplacer et améliorer des éléments des codecs existants. Dans la première étape, nous utilisons une combinaison de modèle source-filtre et de machine à état liquide (LSM), pour démontrer que les caractéristiques liées aux émotions peuvent être facilement extraites et classées à l'aide d'un simple classificateur. Dans la deuxième étape, un seul réseau de bout en bout utilisant une longue mémoire à court terme (LSTM) est utilisé pour produire des trames vocales avec une qualité subjective élevée pour les applications de masquage de perte de paquets (PLC). Dans les dernières étapes, nous nous appuyons sur les résultats des étapes précédentes pour concevoir un codec entièrement basé sur l'apprentissage automatique. un réseau d'encodage, formulé à l'aide d'un réseau neuronal profond (DNN) et entraîné sur plusieurs bases de données publiques, extrait et encode les représentations de la parole en utilisant la prédiction dans un espace latent. Une approche d'apprentissage non supervisé basée sur plusieurs principes de cognition est proposée pour extraire des représentations à partir de trames de parole courtes et longues en utilisant l'information mutuelle et la perte contrastive. La capacité de ces représentations apprises à capturer divers attributs de la parole à court et à long terme est démontrée. Enfin, une structure de décodage est proposée pour synthétiser des signaux de parole à partir de ces représentations. L'entraînement contradictoire est utilisé comme une approximation des mesures subjectives de la qualité de la parole afin de synthétiser des échantillons de parole à consonance naturelle. La haute qualité perceptuelle de la parole synthétisée ainsi obtenue prouve que les représentations extraites sont efficaces pour préserver toutes sortes d'attributs de la parole et donc qu'une méthode de compression complète est démontrée avec l'approche proposée.Abstract: Since the 80s, speech codecs have relied on short-term coding strategies that operate at the subframe or frame level (typically 5 to 20ms). Researchers essentially adjusted and combined a limited number of available technologies (transform, linear prediction, quantization) and strategies (waveform matching, noise shaping) to build increasingly complex coding architectures. In this thesis, rather than relying on short-term coding strategies, we develop an alternative framework for speech compression by encoding speech attributes that are perceptually important characteristics of speech signals. In order to achieve this objective, we solve three problems of increasing complexity, namely classification, prediction and representation learning. Classification is a common element in modern codec designs. In a first step, we design a classifier to identify emotions, which are among the most complex long-term speech attributes. In a second step, we design a speech sample predictor, which is another common element in modern codec designs, to highlight the benefits of long-term and non-linear speech signal processing. Then, we explore latent variables, a space of speech representations, to encode both short-term and long-term speech attributes. Lastly, we propose a decoder network to synthesize speech signals from these representations, which constitutes our final step towards building a complete, end-to-end machine-learning based speech compression method. The first two steps, classification and prediction, provide new tools that could replace and improve elements of existing codecs. In the first step, we use a combination of source-filter model and liquid state machine (LSM), to demonstrate that features related to emotions can be easily extracted and classified using a simple classifier. In the second step, a single end-to-end network using long short-term memory (LSTM) is shown to produce speech frames with high subjective quality for packet loss concealment (PLC) applications. In the last steps, we build upon the results of previous steps to design a fully machine learning-based codec. An encoder network, formulated using a deep neural network (DNN) and trained on multiple public databases, extracts and encodes speech representations using prediction in a latent space. An unsupervised learning approach based on several principles of cognition is proposed to extract representations from both short and long frames of data using mutual information and contrastive loss. The ability of these learned representations to capture various short- and long-term speech attributes is demonstrated. Finally, a decoder structure is proposed to synthesize speech signals from these representations. Adversarial training is used as an approximation to subjective speech quality measures in order to synthesize natural-sounding speech samples. The high perceptual quality of synthesized speech thus achieved proves that the extracted representations are efficient at preserving all sorts of speech attributes and therefore that a complete compression method is demonstrated with the proposed approach

Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-