pwc-archive/papers-with-abstracts huggingface dataset:
Statistical QoS Provisioning Architecture for 6G Satellite-Terrestrial Integrated Networks
Authors: Jingqing Wang, Wenchi Cheng, Wei zhang, Hui Liang
Date: 2024-06-07 00:00:00

The emergence of massive ultra-reliable and low latency communications (mURLLC) as a category of time/reliability-sensitive service over 6G networks has received considerable research attention, which has presented unprecedented challenges. As one of the key enablers for 6G, satellite-terrestrial integrated networks (STIN) have been developed to offer more expansive connectivity and comprehensive 3D coverage in space-aerial-terrestrial domains for supporting 6G mission-critical mURLLC applications while fulfilling diverse and rigorous quality of service (QoS) requirements. In the context of these mURLLC-driven satellite services, data freshness assumes paramount importance, as outdated data may engender unpredictable or catastrophic outcomes. To effectively measure data freshness in satellite-terrestrial integrated communications, age of information (AoI) has recently surfaced as a new dimension of QoS metric to support time-sensitive applications. It is crucial to design new analytical models that ensure stringent and diverse QoS metrics bounded by different key parameters, including AoI, delay, and reliability, over 6G satellite-terrestrial integrated networks. However, due to the complicated and dynamic nature of satellite-terrestrial integrated network environments, the research on efficiently defining new statistical QoS schemes while taking into account varying degrees of freedom has still been in their infancy. To remedy these deficiencies, in this paper we develop statistical QoS provisioning schemes over 6G satellite-terrestrial integrated networks in the finite blocklength regime. Particularly, we firstly introduce and review key technologies for supporting mURLLC. Secondly, we formulate a number of novel fundamental statistical-QoS metrics in the finite blocklength regime. Finally, we conduct a set of simulations to evaluate our developed statistical QoS schemes.
Beyond 5G Network Failure Classification for Network Digital Twin Using Graph Neural Network
Authors: Abubakar Isah, Ibrahim Aliyu, Jaechan Shim, Hoyong Ryu, Jinsul Kim
Date: 2024-06-06 00:00:00
Tasks: Graph Neural Network

Fifth-generation (5G) core networks in network digital twins (NDTs) are complex systems with numerous components, generating considerable data. Analyzing these data can be challenging due to rare failure types, leading to imbalanced classes in multiclass classification. To address this problem, we propose a novel method of integrating a graph Fourier transform (GFT) into a message-passing neural network (MPNN) designed for NDTs. This approach transforms the data into a graph using the GFT to address class imbalance, whereas the MPNN extracts features and models dependencies between network components. This combined approach identifies failure types in real and simulated NDT environments, demonstrating its potential for accurate failure classification in 5G and beyond (B5G) networks. Moreover, the MPNN is adept at learning complex local structures among neighbors in an end-to-end setting. Extensive experiments have demonstrated that the proposed approach can identify failure types in three multiclass domain datasets at multiple failure points in real networks and NDT environments. The results demonstrate that the proposed GFT-MPNN can accurately classify network failures in B5G networks, especially when employed within NDTs to detect failure types.
An Evaluation Benchmark for Autoformalization in Lean4
Authors: Aryan Gulati, Devanshu Ladsaria, Shubhra Mishra, Jasdeep Sidhu, Brando Miranda
Date: 2024-06-01 00:00:00

Large Language Models (LLMs) hold the potential to revolutionize autoformalization. The introduction of Lean4, a mathematical programming language, presents an unprecedented opportunity to rigorously assess the autoformalization capabilities of LLMs. This paper introduces a novel evaluation benchmark designed for Lean4, applying it to test the abilities of state-of-the-art LLMs, including GPT-3.5, GPT-4, and Gemini Pro. Our comprehensive analysis reveals that, despite recent advancements, these LLMs still exhibit limitations in autoformalization, particularly in more complex areas of mathematics. These findings underscore the need for further development in LLMs to fully harness their potential in scientific research and development. This study not only benchmarks current LLM capabilities but also sets the stage for future enhancements in autoformalization.
Language Guided Skill Discovery
Authors: Seungeun Rho, Laura Smith, Tianyu Li, Sergey Levine, Xue Bin Peng, Sehoon Ha
Date: 2024-06-07 00:00:00
Tasks: Diversity

Skill discovery methods enable agents to learn diverse emergent behaviors without explicit rewards. To make learned skills useful for unknown downstream tasks, obtaining a semantically diverse repertoire of skills is essential. While some approaches introduce a discriminator to distinguish skills and others aim to increase state coverage, no existing work directly addresses the "semantic diversity" of skills. We hypothesize that leveraging the semantic knowledge of large language models (LLMs) can lead us to improve semantic diversity of resulting behaviors. In this sense, we introduce Language Guided Skill Discovery (LGSD), a skill discovery framework that aims to directly maximize the semantic diversity between skills. LGSD takes user prompts as input and outputs a set of semantically distinctive skills. The prompts serve as a means to constrain the search space into a semantically desired subspace, and the generated LLM outputs guide the agent to visit semantically diverse states within the subspace. We demonstrate that LGSD enables legged robots to visit different user-intended areas on a plane by simply changing the prompt. Furthermore, we show that language guidance aids in discovering more diverse skills compared to five existing skill discovery methods in robot-arm manipulation environments. Lastly, LGSD provides a simple way of utilizing learned skills via natural language.
Expressive Symbolic Regression for Interpretable Models of Discrete-Time Dynamical Systems
Authors: Adarsh Iyer, Nibodh Boddupalli, Jeff Moehlis
Date: 2024-06-05 00:00:00
Tasks: regression, scientific discovery, Symbolic Regression

Interpretable mathematical expressions defining discrete-time dynamical systems (iterated maps) can model many phenomena of scientific interest, enabling a deeper understanding of system behaviors. Since formulating governing expressions from first principles can be difficult, it is of particular interest to identify expressions for iterated maps given only their data streams. In this work, we consider a modified Symbolic Artificial Neural Network-Trained Expressions (SymANNTEx) architecture for this task, an architecture more expressive than others in the literature. We make a modification to the model pipeline to optimize the regression, then characterize the behavior of the adjusted model in identifying several classical chaotic maps. With the goal of parsimony, sparsity-inducing weight regularization and information theory-informed simplification are implemented. We show that our modified SymANNTEx model properly identifies single-state maps and achieves moderate success in approximating a dual-state attractor. These performances offer significant promise for data-driven scientific discovery and interpretation.
CodecFake: Enhancing Anti-Spoofing Models Against Deepfake Audios from Codec-Based Speech Synthesis Systems
Authors: Haibin Wu, Yuan Tseng, Hung-Yi Lee
Date: 2024-06-11 00:00:00
Tasks: Audio Synthesis, Face Swapping, Speech Synthesis

Current state-of-the-art (SOTA) codec-based audio synthesis systems can mimic anyone's voice with just a 3-second sample from that specific unseen speaker. Unfortunately, malicious attackers may exploit these technologies, causing misuse and security issues. Anti-spoofing models have been developed to detect fake speech. However, the open question of whether current SOTA anti-spoofing models can effectively counter deepfake audios from codec-based speech synthesis systems remains unanswered. In this paper, we curate an extensive collection of contemporary SOTA codec models, employing them to re-create synthesized speech. This endeavor leads to the creation of CodecFake, the first codec-based deepfake audio dataset. Additionally, we verify that anti-spoofing models trained on commonly used datasets cannot detect synthesized speech from current codec-based speech generation systems. The proposed CodecFake dataset empowers these models to counter this challenge effectively.
The Reasonable Effectiveness of Speaker Embeddings for Violence Detection
Authors: Sarthak Jain, Orchid Chetia Phukan, Arun Balaji Buduru, Rajesh Sharma
Date: 2024-06-10 00:00:00
Tasks: Speaker Recognition

In this paper, we focus on audio violence detection (AVD). AVD is necessary for several reasons, especially in the context of maintaining safety, preventing harm, and ensuring security in various environments. This calls for accurate AVD systems. Like many related applications in audio processing, the most common approach for improving the performance, would be by leveraging self-supervised (SSL) pre-trained models (PTMs). However, as these SSL models are very large models with million of parameters and this can hinder real-world deployment especially in compute-constraint environment. To resolve this, we propose the usage of speaker recognition models which are much smaller compared to the SSL models. Experimentation with speaker recognition model embeddings with SVM & Random Forest as classifiers, we show that speaker recognition model embeddings perform the best in comparison to state-of-the-art (SOTA) SSL models and achieve SOTA results.
PERSONA: An Application for Emotion Recognition, Gender Recognition and Age Estimation
Authors: Devyani Koshal, Orchid Chetia Phukan, Sarthak Jain, Arun Balaji Buduru, Rajesh Sharma
Date: 2024-06-10 00:00:00
Tasks: Age Estimation, Emotion Recognition, Multi-Task Learning, Speaker Recognition

Emotion Recognition (ER), Gender Recognition (GR), and Age Estimation (AE) constitute paralinguistic tasks that rely not on the spoken content but primarily on speech characteristics such as pitch and tone. While previous research has made significant strides in developing models for each task individually, there has been comparatively less emphasis on concurrently learning these tasks, despite their inherent interconnectedness. As such in this demonstration, we present PERSONA, an application for predicting ER, GR, and AE with a single model in the backend. One notable point is we show that representations from speaker recognition pre-trained model (PTM) is better suited for such a multi-task learning format than the state-of-the-art (SOTA) self-supervised (SSL) PTM by carrying out a comparative study. Our methodology obviates the need for deploying separate models for each task and can potentially conserve resources and time during the training and deployment phases.
ComFeAT: Combination of Neural and Spectral Features for Improved Depression Detection
Authors: Orchid Chetia Phukan, Sarthak Jain, Shubham Singh, Muskaan Singh, Arun Balaji Buduru, Rajesh Sharma
Date: 2024-06-10 00:00:00
Tasks: Depression Detection

In this work, we focus on the detection of depression through speech analysis. Previous research has widely explored features extracted from pre-trained models (PTMs) primarily trained for paralinguistic tasks. Although these features have led to sufficient advances in speech-based depression detection, their performance declines in real-world settings. To address this, in this paper, we introduce ComFeAT, an application that employs a CNN model trained on a combination of features extracted from PTMs, a.k.a. neural features and spectral features to enhance depression detection. Spectral features are robust to domain variations, but, they are not as good as neural features in performance, suprisingly, combining them shows complementary behavior and improves over both neural and spectral features individually. The proposed method also improves over previous state-of-the-art (SOTA) works on E-DAIC benchmark.
Sample Rate Independent Recurrent Neural Networks for Audio Effects Processing
Authors: Alistair Carson, Alec Wright, Jatin Chowdhury, Vesa Välimäki, Stefan Bilbao
Date: 2024-06-10 00:00:00

In recent years, machine learning approaches to modelling guitar amplifiers and effects pedals have been widely investigated and have become standard practice in some consumer products. In particular, recurrent neural networks (RNNs) are a popular choice for modelling non-linear devices such as vacuum tube amplifiers and distortion circuitry. One limitation of such models is that they are trained on audio at a specific sample rate and therefore give unreliable results when operating at another rate. Here, we investigate several methods of modifying RNN structures to make them approximately sample rate independent, with a focus on oversampling. In the case of integer oversampling, we demonstrate that a previously proposed delay-based approach provides high fidelity sample rate conversion whilst additionally reducing aliasing. For non-integer sample rate adjustment, we propose two novel methods and show that one of these, based on cubic Lagrange interpolation of a delay-line, provides a significant improvement over existing methods. To our knowledge, this work provides the first in-depth study into this problem.
Emotion-Aware Speech Self-Supervised Representation Learning with Intensity Knowledge
Authors: Rui Liu, Zening Ma
Date: 2024-06-10 00:00:00
Tasks: Representation Learning, Self-Supervised Learning, Speech Representation Learning

Speech Self-Supervised Learning (SSL) has demonstrated considerable efficacy in various downstream tasks. Nevertheless, prevailing self-supervised models often overlook the incorporation of emotion-related prior information, thereby neglecting the potential enhancement of emotion task comprehension through emotion prior knowledge in speech. In this paper, we propose an emotion-aware speech representation learning with intensity knowledge. Specifically, we extract frame-level emotion intensities using an established speech-emotion understanding model. Subsequently, we propose a novel emotional masking strategy (EMS) to incorporate emotion intensities into the masking process. We selected two representative models based on Transformer and CNN, namely MockingJay and Non-autoregressive Predictive Coding (NPC), and conducted experiments on IEMOCAP dataset. Experiments have demonstrated that the representations derived from our proposed method outperform the original model in SER task.
Soundscape Captioning using Sound Affective Quality Network and Large Language Model
Authors: Yuanbo Hou, Qiaoqiao Ren, Andrew Mitchell, Wenwu Wang, Jian Kang, Tony Belpaeme, Dick Botteldooren
Date: 2024-06-09 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

We live in a rich and varied acoustic world, which is experienced by individuals or communities as a soundscape. Computational auditory scene analysis, disentangling acoustic scenes by detecting and classifying events, focuses on objective attributes of sounds, such as their category and temporal characteristics, ignoring their effects on people, such as the emotions they evoke within a context. To fill this gap, we propose the soundscape captioning task, which enables automated soundscape analysis, thus avoiding labour-intensive subjective ratings and surveys in conventional methods. With soundscape captioning, context-aware descriptions are generated for soundscape by capturing the acoustic scene, event information, and the corresponding human affective qualities (AQs). To this end, we propose an automatic soundscape captioner (SoundSCaper) system composed of an acoustic model, i.e. SoundAQnet, and a large language model (LLM). SoundAQnet simultaneously models multi-scale information about acoustic scenes, events, and perceived AQs, while the LLM describes the soundscape with captions by parsing the information captured with SoundAQnet. The soundscape caption's quality is assessed by a jury of 16 audio/soundscape experts. The average score (out of 5) of SoundSCaper-generated captions is lower than the score of captions generated by two soundscape experts by 0.21 and 0.25, respectively, on the evaluation set and the model-unknown mixed external dataset with varying lengths and acoustic properties, but the differences are not statistically significant. Overall, the proposed SoundSCaper shows promising performance, with captions generated being comparable to those annotated by soundscape experts. The code of models, LLM scripts, human assessment data and instructions, and expert evaluation statistics are all publicly available.
Sparse Binarization for Fast Keyword Spotting
Authors: Jonathan Svirsky, Uri Shaham, Ofir Lindenbaum
Date: 2024-06-09 00:00:00
Tasks: Binarization, Keyword Spotting

With the increasing prevalence of voice-activated devices and applications, keyword spotting (KWS) models enable users to interact with technology hands-free, enhancing convenience and accessibility in various contexts. Deploying KWS models on edge devices, such as smartphones and embedded systems, offers significant benefits for real-time applications, privacy, and bandwidth efficiency. However, these devices often possess limited computational power and memory. This necessitates optimizing neural network models for efficiency without significantly compromising their accuracy. To address these challenges, we propose a novel keyword-spotting model based on sparse input representation followed by a linear classifier. The model is four times faster than the previous state-of-the-art edge device-compatible model with better accuracy. We show that our method is also more robust in noisy environments while being fast. Our code is available at: https://github.com/jsvir/sparknet.
Towards Lightweight Speaker Verification via Adaptive Neural Network Quantization
Authors: Bei Liu, Haoyu Wang, Yanmin Qian
Date: 2024-06-08 00:00:00
Tasks: Quantization, Speaker Verification

Modern speaker verification (SV) systems typically demand expensive storage and computing resources, thereby hindering their deployment on mobile devices. In this paper, we explore adaptive neural network quantization for lightweight speaker verification. Firstly, we propose a novel adaptive uniform precision quantization method which enables the dynamic generation of quantization centroids customized for each network layer based on k-means clustering. By applying it to the pre-trained SV systems, we obtain a series of quantized variants with different bit widths. To enhance the performance of low-bit quantized models, a mixed precision quantization algorithm along with a multi-stage fine-tuning (MSFT) strategy is further introduced. Unlike uniform precision quantization, mixed precision approach allows for the assignment of varying bit widths to different network layers. When bit combination is determined, MSFT is employed to progressively quantize and fine-tune network in a specific order. Finally, we design two distinct binary quantization schemes to mitigate performance degradation of 1-bit quantized models: the static and adaptive quantizers. Experiments on VoxCeleb demonstrate that lossless 4-bit uniform precision quantization is achieved on both ResNets and DF-ResNets, yielding a promising compression ratio of around 8. Moreover, compared to uniform precision approach, mixed precision quantization not only obtains additional performance improvements with a similar model size but also offers the flexibility to generate bit combination for any desirable model size. In addition, our suggested 1-bit quantization schemes remarkably boost the performance of binarized models. Finally, a thorough comparison with existing lightweight SV systems reveals that our proposed models outperform all previous methods by a large margin across various model size ranges.
Diversifying and Expanding Frequency-Adaptive Convolution Kernels for Sound Event Detection
Authors: Hyeonuk Nam, Seong-Hu Kim, Deokki Min, Junhyeok Lee, Yong-Hwa Park
Date: 2024-06-08 00:00:00
Tasks: Event Detection, Sound Event Detection

Frequency dynamic convolution (FDY conv) has shown the state-of-the-art performance in sound event detection (SED) using frequency-adaptive kernels obtained by frequency-varying combination of basis kernels. However, FDY conv lacks an explicit mean to diversify frequency-adaptive kernels, potentially limiting the performance. In addition, size of basis kernels is limited while time-frequency patterns span larger spectro-temporal range. Therefore, we propose dilated frequency dynamic convolution (DFD conv) which diversifies and expands frequency-adaptive kernels by introducing different dilation sizes to basis kernels. Experiments showed advantages of varying dilation sizes along frequency dimension, and analysis on attention weight variance proved dilated basis kernels are effectively diversified. By adapting class-wise median filter with intersection-based F1 score, proposed DFD-CRNN outperforms FDY-CRNN by 3.12% in terms of polyphonic sound detection score (PSDS).
LDM-SVC: Latent Diffusion Model Based Zero-Shot Any-to-Any Singing Voice Conversion with Singer Guidance
Authors: Shihao Chen, Yu Gu, Jie Zhang, Na Li, Rilin Chen, Liping Chen, LiRong Dai
Date: 2024-06-08 00:00:00
Tasks: Voice Conversion

Any-to-any singing voice conversion (SVC) is an interesting audio editing technique, aiming to convert the singing voice of one singer into that of another, given only a few seconds of singing data. However, during the conversion process, the issue of timbre leakage is inevitable: the converted singing voice still sounds like the original singer's voice. To tackle this, we propose a latent diffusion model for SVC (LDM-SVC) in this work, which attempts to perform SVC in the latent space using an LDM. We pretrain a variational autoencoder structure using the noted open-source So-VITS-SVC project based on the VITS framework, which is then used for the LDM training. Besides, we propose a singer guidance training method based on classifier-free guidance to further suppress the timbre of the original singer. Experimental results show the superiority of the proposed method over previous works in both subjective and objective evaluations of timbre similarity.
Signal processing algorithm effective for sound quality of hearing loss simulators
Authors: Toshio Irino, Shintaro Doan, Minami Ishikawa
Date: 2024-06-07 00:00:00

Hearing loss (HL) simulators, which allow normal hearing (NH) listeners to experience HL, have been used in speech intelligibility experiments, but not in sound quality experiments due to perceptible distortion. If they produced less distortion, they might be useful for NH listeners to evaluate the sound quality of, for example, hearing aids. We conducted perceptual sound quality experiments to compare the Cambridge version of HL simulator (CamHLS) and the Wakayama version of the HL simulator (WHIS), which has the two algorithms of filterbank analysis synthesis (FBAS) and direct time-varying filter (DTVF). The experimental results showed that WHIS with DTVF produces less perceptible distortion in speech sounds than CamHLS and WHIS with FBAS, even when the nonlinear process is working. This advantage is mainly due to the use of the DTVF algorithm, which could be applied to various signal synthesis applications with filterbank analysis.