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The Latest News, Articles, and Events in Signal Processing
Although supervised deep learning has revolutionized speech and audio processing, it has necessitated the building of specialist models for individual tasks and application scenarios. It is likewise difficult to apply this to dialects and languages for which only limited labeled data is available. Self-supervised representation learning methods promise a single universal model that would benefit a wide variety of tasks and domains.
The papers in this special section focus on self-supervised learning for speech and audio processing. A current trend in the machine learning community is the adoption of self-supervised approaches to pretrain deep networks. Self-supervised learning utilizes proxy-supervised learning tasks (or pretext tasks) - for example, distinguishing parts of the input signal from distractors or reconstructing masked input segments conditioned on unmasked segments—to obtain training data from unlabeled corpora.
The IEEE SPS congratulates the following SPS members who will receive the Society’s prestigious awards during ICASSP 2023 in Greece.
Novel computational signal and image analysis approaches based on feature-rich mathematical/computational frameworks continue to push the limits of the technological envelope, thus providing optimized and efficient solutions.
IEEE SPS has built a streamlined mechanism for employers to add a job announcement by simply filling in a simple job opportunity submission Web form related to a particular TC field. To submit job announcements for a particular Technical Committee, the submission form can be found by visiting the page below and selecting a particular TC.
The Signal Processing Society (SPS) has 12 Technical Committees that support a broad selection of signal processing-related activities defined by the scope of the Society.
Each year, the IEEE Board of Directors confers the grade of Fellow on up to one-tenth of one percent of the voting members. To qualify for consideration, an individual must have been a Member, normally for five years or more, and a Senior Member at the time for nomination to Fellow. The grade of Fellow recognizes unusual distinction in IEEE’s designated fields.
The IEEE Signal Processing Society (SPS) announces the 2023 Class of Distinguished Lecturers and Distinguished Industry Speakers for the term of 1 January 2023 to 31 December 2024. The IEEE SPS Distinguished Lecturer (DL) Program provides a means for Chapters to have access to well-known educators and authors in the fields of signal processing to lecture at Chapter meetings.
Date: 10-13 December 2023
Location: Los Sueños, Costa Rica
Date: 17-20 September 2023
Location: Rome, Italy
Date: 22-25 October 2023
Location: New Paltz, NY, USA
Submission Deadline: 3 February 2023
Call for Proposal Document
Are you looking for innovative ways to energize and collaborate with your local signal processing community? Consider hosting a Seasonal School or Member Driven Initiative!
March 21-24, 2023
Location: Snowbird, UT, USA
Date: 15-17 February 2023
Registeration Deadline: N/A
Location: Pakistan
Date: 5-10 December 2022
Registeration Deadline: 25 November 2022
Location: Andhra Pradesh, India
Manuscript Due: 1 March 2023
Publication Date: July 2024
CFP Document
Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music classification, speech emotion classification and sound event detection. In this blog, we introduce pretrained audio neural networks (PANNs) trained on the large-scale AudioSet dataset. These PANNs are transferred to other audio related tasks. We investigate the performance and computational complexity of PANNs modeled by a variety of convolutional neural networks. We propose an architecture called Wavegram-Logmel-CNN using both log-mel spectrogram and waveform as input feature.
Recent years, face recognition has made a remarkable breakthrough due to the emergence of deep learning. However, compared with frontal face recognition, many deep face recognition models still suffer serious performance degradation when handling profile faces. To address this issue, we propose a novel Frontal-Centers Guided Loss (FCGFace) to obtain highly discriminative features for face recognition. Most existing discriminative feature learning approaches project features from the same class into a separated latent subspace.
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