L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing

The L3DAS21 Challenge is aimed at encouraging and fostering collaborative research on machine learning for 3D audio signal processing, with particular focus on 3D speech enhancement (SE) and 3D sound localization and detection (SELD). Alongside with the challenge, we release the L3DAS21 dataset, a 65 hours 3D audio corpus, accompanied with a Python API that facilitates the data usage and results submission stage. Usually, machine learning approaches to 3D audio tasks are based on single-perspective Ambisonics recordings or on arrays of single-capsule microphones. We propose, instead, a novel multichannel audio configuration based multiple-source and multiple-perspective Ambisonics recordings, performed with an array of two first-order Ambisonics microphones. To the best of our knowledge, it is the first time that a dual-mic Ambisonics configuration is used for these tasks. We provide baseline models and results for both tasks, obtained with state-of-the-art architectures: FaSNet for SE and SELDNet for SELD. This report is aimed at providing all needed information to participate in the L3DAS21 Challenge, illustrating the details of the L3DAS21 dataset, the challenge tasks and the baseline models.Comment: Documentation paper for the L3DAS21 Challenge for IEEE MLSP 2021. Further information on www.l3das.com/mlsp202

Contextual bandits algorithms for reconfigurable hardware accelerators

Reconfigurable processing cores for IoT and edge computing applications are emerging topics to calibrate costs, energy consumption and area occupation with performance and reliability on Commercial Off the Shelf (COTS) devices. This work analyzes how to take advantage of Machine Learning to potentially automate the reconfiguration process of a hardware accelerator inside the Klessydra Vector Coprocessor Unit (VCU), choosing the best configuration according to the workload. The problem is modeled with a contextual bandits approach using the Linear UCB algorithms and validated with offline Python simulations

Implementation of dynamic acceleration unit exchange on a RISC-V soft-processor

Using Artificial Intelligence (AI) techniques has become the best solution in many applications. By the end of Moore's Law, implementing a platform capable of such massive processing for edge-IoT applications has become a significant challenge. However, using static hardware accelerators can be an excellent solution; even so, they typically require a great deal of silicon area and are not optimized for all operation modes. Reconfigurable computing lets parts of the hardware change proportionally to the task during operation, allowing for optimized operation and the use of many hardware accelerators without requiring a large area. In this study, we present a dynamic acceleration unit exchange on a RISC-V soft-processor based on the open-source Klessydra-T13 RISC-V core. We show how reconfiguration can be used to make the hardware accelerator more flexible and improve its performance. As a case study, we show how reconfiguration techniques can be used to speed up AI architectures by reconfiguration of vector accelerator units

TCDABCF: A Trust-Based Community Detection Using Artificial Bee Colony by Feature Fusion

Social network aims to extend a widespread framework to communicate users and find alike people with common features, easier and faster. As people usually experience in everyday life, social communication can be formed from common groups with almost identical properties. Detecting such groups or communities is a challenging task in various fields of social network analysis. Many researchers intend to develop algorithms that work effectively and efficiently on social networks. It is believed that the most influential user in a community that had been followed by similar users could be a central point of a community or cluster, and the similar user would be members of the community. Research studies tend to increase intracommunity similarity and decrease intercommunity similarity to improve the performance of the community detection methods by finding such influential users accurately. In this paper, a hybrid metaheuristic method is proposed. In the proposed method called trust-based community detection using artificial bee colony by feature fusion (TCDABCF), we use a fusion approach combined with artificial bee colony (ABC) to improve the accuracy of the community detection task. In this approach, not only the social features of users are considered but also the relationship of trust between users in a community is also calculated. So, the proposed method can lead to finding more precise clusters of similar users with influential users in the center of each cluster. The proposed method uses the artificial bee colony (ABC) to find the influential users and the relation of their followers accurately. We compare this algorithm with nine state-of-the-art methods on the Facebook dataset. Experimental results show that the proposed method has obtained values of 0.9662 and 0.9533 for NMI and accuracy, respectively, which has improved in comparison with state-of-the-art community detection methods

L3DAS21 challenge: machine learning for 3D audio signal processing

The L3DAS21 Challenge11www.13das.com/mlsp2021 is aimed at encouraging and fostering collaborative research on machine learning for 3D audio signal processing, with particular focus on 3D speech enhancement (SE) and 3D sound localization and detection (SELD). Alongside with the challenge, we release the L3DAS21 dataset, a 65 hours 3D audio corpus, accompanied with a Python API that facilitates the data usage and results submission stage. Usually, machine learning approaches to 3D audio tasks are based on single-perspective Ambisonics recordings or on arrays of single-capsule microphones. We propose, instead, a novel multichannel audio configuration based multiple-source and multiple-perspective Ambisonics recordings, performed with an array of two first-order Ambisonics microphones. To the best of our knowledge, it is the first time that a dualmic Ambisonics configuration is used for these tasks. We provide baseline models and results for both tasks, obtained with state-of-The-Art architectures: FaSNet for SE and SELDnet for SELD