Characterisation of noisy speech channels in 2G and 3G mobile networks

As the wireless cellular market reaches competitive levels never seen before, network operators need to focus on maintaining Quality of Service (QoS) a main priority if they wish to attract new subscribers while keeping existing customers satisfied. Speech Quality as perceived by the end user is one major example of a characteristic in constant need of maintenance and improvement. It is in this topic that this Master Thesis project fits in. Making use of an intrusive method of speech quality evaluation, as a means to further study and characterize the performance of speech codecs in second-generation (2G) and third-generation (3G) technologies. Trying to find further correlation between codecs with similar bit rates, along with the exploration of certain transmission parameters which may aid in the assessment of speech quality. Due to some limitations concerning the audio analyzer equipment that was to be employed, a different system for recording the test samples was sought out. Although the new designed system is not standard, after extensive testing and optimization of the system's parameters, final results were found reliable and satisfactory. Tests include a set of high and low bit rate codecs for both 2G and 3G, where values were compared and analysed, leading to the outcome that 3G speech codecs perform better, under the approximately same conditions, when compared with 2G. Reinforcing the idea that 3G is, with no doubt, the best choice if the costumer looks for the best possible listening speech quality. Regarding the transmission parameters chosen for the experiment, the Receiver Quality (RxQual) and Received Energy per Chip to the Power Density Ratio (Ec/N0), these were subject to speech quality correlation tests. Final results of RxQual were compared to those of prior studies from different researchers and, are considered to be of important relevance. Leading to the confirmation of RxQual as a reliable indicator of speech quality. As for Ec/N0, it is not possible to state it as a speech quality indicator however, it shows clear thresholds for which the MOS values decrease significantly. The studied transmission parameters show that they can be used not only for network management purposes but, at the same time, give an expected idea to the communications engineer (or technician) of the end-to-end speech quality consequences. With the conclusion of the work new ideas for future studies come to mind. Considering that the fourth-generation (4G) cellular technologies are now beginning to take an important place in the global market, as the first all-IP network structure, it seems of great relevance that 4G speech quality should be subject of evaluation. Comparing it to 3G, not only in narrowband but also adding wideband scenarios with the most recent standard objective method of speech quality assessment, POLQA. Also, new data found on Ec/N0 tests, justifies further research studies with the intention of validating the assumptions made in this work.Com o mercado das redes móveis a atingir níveis de competitividade nunca antes vistos, existe a crescente necessidade por parte dos operadores de rede em focar-se na Qualidade de Serviço (QoS) como principal prioridade, no sentido de atrair novos clientes ao mesmo tempo que asseguram a satisfação dos seus actuais assinantes. A percepção da Qualidade de Voz, por parte do utilizador, é apenas um exemplo de uma característica de QoS em constante necessidade de manutenção e melhoramento. Sendo nesta temática em que se insere a Tese de Mestrado. Aplicando um método intrusivo de avaliação de qualidade de voz, como meio para um estudo mais aprofundado e, ao mesmo tempo, caracterizando o desempenho dos codecs de voz para as tecnologias de segunda-geração (2G) e terceira-geração (3G). Investigando nova informação que possa ser retirada da correlação entre codecs com bit rates semelhantes, juntamente com a exploração de determinados 'parâmetros de transmissão os quais podem auxiliar na avaliação da qualidade de voz. Devido a algumas limitações ligadas ao analisador de áudio (requisito neste tipo de aplicações), existiu a necessidade de procurar um sistema distinto para gravação das amostras de teste. Embora o sistema escolhido não seja padronizado para este tipo de ensaios, após vários testes e consequente optimização dos parâmetros do sistema, os resultados finais consideram-se credíveis e satisfatórios. Os testes efectuados incluem um conjunto de codecs de elevado e baixo bit rate, onde a comparação e análise dos resultados levam a concluir que codecs de voz 3G têm melhor desempenho, sob aproximadamente as mesmas condições, comparativamente com os 2G. Reforçando a ideia generalizada que 3G é, sem dúvida, a melhor escolha se o utilizador procura uma solução superior a nível de qualidade de voz. No que diz respeito aos parâmetros de transmissão escolhidos para a experiência, RxQual (Qualidade do sinal Recebido pela estacão móvel) e Ec/N0 (razão entre Energia por chip e a Densidade Espectral de Potência), estes foram sujeitos a testes de correlação com a qualidade de voz. Os resultados de RxQual foram sujeitos a comparação com estudos prévios de outros investigadores, confirmando este parâmetro como um indicador de qualidade de voz bastante fiável. Quanto a Ec/N0, não é possível declará-lo como um indicador de qualidade de voz, no entanto, este demonstra limites claros para os quais os valores de Mean Opinion Score (MOS) decrescem significativamente. Os parâmetros de transmissão estudados demonstram não só que podem ser utilizados com objectivos de gestão de rede mas como também podem fornecer, ao engenheiro (ou técnico), informação relativa ao impacto que poderá existir na qualidade de voz. Com a finalização deste trabalho é possível constatar que novos estudos devem ser efectuados. Considerando que a tecnologia de quarta-geração (4G) começa agora a dar os seus primeiros passos no mercado das redes móveis, como a primeira com arquitectura de rede totalmente orientada para IP, parece de grande importância que esta tecnologia seja sujeita a avaliação. Comparando-a com 3G, não só para banda-estreita (300 a 3400 Hz) como também para cenários de banda-larga (50 a 7000Hz), aplicando o mais recente método normalizado de avaliação de qualidade de voz, o POLQA. Por fim, também se verifica como pertinente uma continuação do estudo relativo a Ec/N0 a fim de validar as ilações retiradas neste trabalho

Media gateway utilizando um GPU

Mestrado em Engenharia de Computadores e Telemátic

Machine Learning for Beamforming in Audio, Ultrasound, and Radar

Multi-sensor signal processing plays a crucial role in the working of several everyday technologies, from correctly understanding speech on smart home devices to ensuring aircraft fly safely. A specific type of multi-sensor signal processing called beamforming forms a central part of this thesis. Beamforming works by combining the information from several spatially distributed sensors to directionally filter information, boosting the signal from a certain direction but suppressing others. The idea of beamforming is key to the domains of audio, ultrasound, and radar. Machine learning is the other central part of this thesis. Machine learning, and especially its sub-field of deep learning, has enabled breakneck progress in tackling several problems that were previously thought intractable. Today, machine learning powers many of the cutting edge systems we see on the internet for image classification, speech recognition, language translation, and more. In this dissertation, we look at beamforming pipelines in audio, ultrasound, and radar from a machine learning lens and endeavor to improve different parts of the pipelines using ideas from machine learning. We start off in the audio domain and derive a machine learning inspired beamformer to tackle the problem of ensuring the audio captured by a camera matches its visual content, a problem we term audiovisual zooming. Staying in the audio domain, we then demonstrate how deep learning can be used to improve the perceptual qualities of speech by denoising speech clipping, codec distortions, and gaps in speech. Transitioning to the ultrasound domain, we improve the performance of short-lag spatial coherence ultrasound imaging by exploiting the differences in tissue texture at each short lag value by applying robust principal component analysis. Next, we use deep learning as an alternative to beamforming in ultrasound and improve the information extraction pipeline by simultaneously generating both a segmentation map and B-mode image of high quality directly from raw received ultrasound data. Finally, we move to the radar domain and study how deep learning can be used to improve signal quality in ultra-wideband synthetic aperture radar by suppressing radio frequency interference, random spectral gaps, and contiguous block spectral gaps. By training and applying the networks on raw single-aperture data prior to beamforming, it can work with myriad sensor geometries and different beamforming equations, a crucial requirement in synthetic aperture radar

Semantic Communications for Speech Transmission

Wireless communication systems have undergone vigorous advancements from the first generation (1G) to the fifth generation (5G) over the past few decades by developing numerous coding algorithms and channel models to recover accurate sources at the bit level. However, in recent years, the flourishing of artificial intelligence (AI) has revolutionised various industries and incubated multifarious intelligent tasks, which increases the amount of data transmission to the zetta-byte level and requires massive machine connectivity with low transmission latency and energy consumption. In this context, conventional communication systems are facing severe challenges imposed by ubiquitous AI tasks. Therefore, it is inevitable to develop a new communication paradigm. Semantic communications have been proposed to address the challenges by extracting semantic information inherent in source data while omitting irrelative redundant information to reduce the transmission data, thereby lowering communication resources and facilitating high semantic fidelity transmission. Nevertheless, the exploration of semantic communications has gone through decades of stagnation since it was first identified because of the inadequacy of mathematical models for semantic information. Inspired by the thriving of AI, deep learning (DL)-enabled semantic communications have been scrutinised as promising solutions to the bottlenecks in conventional communications by leveraging the learning and fitting capabilities of neural networks to bypass mathematical models for semantic extraction and representation. To this end, this thesis explores DL-enabled semantic communications for speech transmission to tackle technical problems in conventional speech communication networks, including semantic-agnostic coding algorithms, unreliable speech transmission in complicated channel environments, single system output limited to the speech modality, and speech quality susceptible to external interferences. Specifically, a general semantic communication system for speech transmission over single-input single-output (SISO) channels, named DeepSC-S, is first developed to reconstruct speech information by transmitting global semantic features. In addition, the system output is extended to multimodal data across different languages by introducing a task-oriented semantic communication framework for speech transmission, named DeepSC-ST, to perform various downstream intelligent tasks, including speech recognition, speech synthesis, speech-to-text translation (S2TT), and speech-to-speech translation (S2ST). Moreover, the endeavours towards semantic communications for speech transmission over multiple-input multiple-output (MIMO) channels are carried out to contend with practical communication scenarios, and a semantic-aware network is devised to improve the performance of intelligent tasks. Furthermore, the realistic scenarios involving corrupted speech input due to external inferences are further considered by establishing a semantic impairment suppression mechanism to compensate for impaired semantics in the corrupted speech and to facilitate robust end-to-end (E2E) semantic communications for speech-to-text translation. The proposed DeepSC-S and its variants investigated in this thesis demonstrate high proficiency in semantic communications for speech transmission by reducing substantial transmission data, performing diverse semantic tasks, providing superior system performance, and tolerating dynamic channel effects