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We consider a cell-free massive multiple-input multiple-output (CF-MaMIMO) communication system in the uplink, which is particularly prone to the detrimental effects of pilot contamination (PC), a phenomenon that occurs when pilot sequences assigned to user equipments (UEs) are not mutually orthogonal, eventually due to their reuse. To combat PC, the presenters propose a novel algorithm for blind or semi-blind joint channel estimation and data detection (JCD).

There is a fast development of different machine learning methods – for object classification, tracking, action recognition and other tasks with multiple types of data – from images and videos to data from wireless sensor networks. Autonomous image and video analytics face a number of challenges due to the huge volumes of data that sensors provide, the changeable environmental conditions and other factors. However, it is important to know when the methods work well and when they are not reliable, e.g. how much could we trust the obtained results?

The next generation of cellular networks promises to revolutionize wireless communication by creating smart radio environments (SREs) through the use of reconfigurable intelligent surfaces (RISs). These RISs offer significant benefits, including the ability to optimize the radio channel for specific applications, thereby enhancing overall system performance. For instance, RIS elements can be reconfigured to achieve precise localization and sensing, while simultaneously improving communications in cellular networks.

This presentation explores recent advancements in integrating traditional signal processing with deep learning to address the challenges of acoustic feedback cancellation, particularly focusing on acoustic echo and howling suppression. While traditional methods typically rely on adaptive filters to mitigate these issues, their performance is often limited by nonlinear distortions. With the rapid progress in deep learning, numerous studies have proposed solutions that either leverage deep learning independently or combine it with conventional adaptive filtering techniques.

The journey begins with a familiar parable: A group of villagers hears that a strange and enormous creature from India has arrived. Eager to understand it, they sneak into the dark barn where it’s kept. One touches the trunk and says, “It’s like a snake!” Another, feeling a leg, declares, “No, it’s like a tree trunk!” A third, touching an ear, insists, “It’s like a fan!” Who is right? The truth is, none of them sees the whole elephant. But what if they had more points of contact?

This webinar, part of the IEEE JSTSP webinar series, will focus on a recent special issue on Neural Speech and Audio Coding (NSAC}, a rapidly emerging research field at the intersection of signal processing and artificial intelligence (AI). Recent advances in deep neural networks and data-driven methodologies have opened new possibilities for unprecedented coding gain, which is far beyond the capabilities of traditional codecs.

Sound field estimation, also referred to as sound field reconstruction, capturing, and interpolation, is a fundamental problem in acoustic signal processing that aims to reconstruct a spatial acoustic field from a discrete set of microphone measurements. Its wide range of applications includes the visualization/auralization of an acoustic field, spatial audio reproduction using a loudspeaker array or headphones, and active noise cancellation in a spatial region.

The Integrated Sensing and Communications (ISAC) paradigm is anticipated to be a cornerstone of the upcoming 6G networks. In order to optimize the use of wireless resources, 6G ISAC systems need to harness the communication data payload signals, which are inherently random, for both sensing and communication (S&C) purposes. This talk will provide a comprehensive technical overview of the signal processing methodologies for ISAC transmission with random communication signals.

Speech can easily be manipulated through techniques, such as text-to-speech synthesis, voice conversion, replay, tampering, adversarial attacks, and more. However, when the manipulation is applied only to a minor portion of an audio, the remaining real segments can have a dominant influence upon human listeners and make machine detection extremely challenging. Therefore, there is an urgent need to explore such a scenario, where synthetic speech is embedded within otherwise real audio.

The digitization of information has profoundly transformed human lives. At the core of this process lies quantization—the rounding of time and amplitude by analog-to-digital converters, a concept taught widely in engineering education. The Shannon–Nyquist theorem (1949) proves that time quantization can be lossless, but amplitude quantization introduces quantization noise, exposing a fundamental trade-off between digital resolution and dynamic range. Can we recover a signal from quantization noise alone?