Connectionists: CFP: IEEE JSTSP Special Issue - deadline extended till 11 Nov 2024

[Amir Hussain]

Dear connectionists (with apologies for any cross-postings)

Due to numerous requests from authors, the deadline for the Special
Issue (SI) of the IEEE Journal of Selected Topics in Signal Processing
(JSTSP) on "Deep Multimodal Speech Enhancement and Separation" has
been extended till 11 November 2024 - see CFP below and here:
https://signalprocessingsociety.org/publications-resources/special-issue-deadlines/ieee-jstsp-special-issue-deep-multimodal-speech-enhancement-and-separation

CFP: IEEE Journal of Selected Topics in Signal Processing (JSTSP)
Special Issue (SI) on: Deep Multimodal Speech Enhancement and Separation

Manuscripts Due: 11 November 2024 (final extension)
SI Publication Date: May 2025

Scope:

Voice is the most commonly used modality by humans to communicate and
psychologically blend into society. Recent technological advances have
triggered the development of various voice-related applications in the
information and communications technology market. However, noise,
reverberation, and interfering speech are detrimental for effective
communications between humans and other humans or machines, leading to
performance degradation of associated voice-enabled services. To
address the formidable speech-in-noise challenge, a range of speech
enhancement (SE) and speech separation (SS) techniques are normally
employed as important front-end speech processing units to handle
distortions in input signals in order to provide more intelligible
speech for automatic speech recognition (ASR), synthesis and dialogue
systems. Emerging advances in artificial intelligence (AI) and machine
learning, particularly deep neural networks, have led to remarkable
improvements in SE and SS based solutions. A growing number of
researchers have explored various extensions of these methods by
utilising a variety of modalities as auxiliary inputs to the main
speech processing task to access additional information from
heterogeneous signals. In particular, multi-modal SE and SS systems
have been shown to deliver enhanced performance in challenging noisy
environments by augmenting the conventional speech modality with
complementary information from multi-sensory inputs, such as video,
noise type, signal-to-noise ratio (SNR), bone-conducted speech
(vibrations), speaker, text information, electromyography, and
electromagnetic midsagittal articulometer (EMMA) data. Various
integration schemes, including early and late fusions, cross-attention
mechanisms, and self-supervised learning algorithms, have also been
successfully explored.

Topics:

This timely special issue aims to collate latest advances in
multi-modal SE and SS systems that exploit both conventional and
unconventional modalities to further improve state-of-the-art
performance in benchmark problems. We particularly welcome submissions
for novel deep neural network based algorithms and architectures,
including new feature processing methods for multimodal and
cross-modal speech processing. We also encourage submissions that
address practical issues related to multimodal data recording,
energy-efficient system design and real-time low-latency solutions,
such as for assistive hearing and speech communication applications.

Special Issue research topics of interest relate to open problems
needing addressed These include, but are not limited to, the
following.
- Novel acoustic features and architectures for multi-modal SE (MM-SE)
and multi-modal SS (MM-SS) solutions.
- Self-supervised and unsupervised learning techniques for MM-SE and
MM-SS systems.
- Adversarial learning for MM-SE and MM-SS.
- Large language model-based Generative approaches for MM-SE and MM-SS
- Low-delay, low-power, low-complexity MM-SE and MM-SS models
- Integration of multiple data acquisition devices for multimodal
learning and novel learning algorithms robust to imperfect data.
- Few-shot/zero-shot learning and adaptation algorithms for MM-SE and
MM-SS systems with a small amount of training and adaptation data.
- Approaches that effectively reduce model size and inference cost
without reducing the speech quality and intelligibility of processed
signals.
- Novel objective functions including psychoacoustics and perceptually
motivated loss functions for MM-SE and MM-SS
- Holistic evaluation metrics for MM-SE and MM-SS systems.
- Real-world applications and use-cases of MM-SE and MM-SS, including
human-human and human-machine communications
- Challenges and solutions in the integration of MM-SE and MM-SS into
existing systems

We encourage submissions that not only propose novel approaches but
also substantiate the findings with rigorous evaluations, including
real-world datasets. Studies that provide insights into the challenges
involved and the impact of MM-SE and MM-SS on end-users are
particularly welcome.

Submission Guidelines:

Manuscripts should be original and should not have been previously
published or currently under consideration for publication elsewhere.
All submissions will be peer-reviewed according to the IEEE Signal
Processing Society review process. Authors should prepare their
manuscripts according to the Instructions for Authors available from
the Signal Processing Society website.

Important Dates

Manuscript Submission Deadline: 11 November 2024
First Review Due: 15 December 2024
Revised Manuscript Due: 15 January 2024
Second Review Due: 15 February 2024
Final Decision: 28 February 2025

Guest Editors:
Amir Hussain, Edinburgh Napier University, UK
Yu Tsao, Academia Sinica, Taiwan
John H.L. Hansen, University of Texas at Dallas, USA
Naomi Harte, Trinity College Dublin, Ireland
Shinji Watanabe, Carnegie Mellon University, USA
Isabel Trancoso, Instituto Superior Técnico, IST, Univ. Lisbon, Portugal
Shixiong Zhang, Tencent AI Lab, USA

We look forward to your submissions.

Many thanks,

On behalf of the Guest Editorial Team

--
Prof Amir Hussain
School of Computing,
Edinburgh Napier University, Scotland, UK
E-mail: A.Hussain at napier.ac.uk
http://cogmhear.org
https://www.napier.ac.uk/people/amir-hussain