pwc-archive/papers-with-abstracts huggingface dataset:
Variational Bayesian Inference Clustering Based Joint User Activity and Data Detection for Grant-Free Random Access in mMTC
Authors: Zhaoji Zhang, Qinghua Guo, Ying Li, Ming Jin, Chongwen Huang
Date: 2022-10-25 00:00:00
Tasks: Bayesian Inference, Clustering, Scheduling

Tailor-made for massive connectivity and sporadic access, grant-free random access has become a promising candidate access protocol for massive machine-type communications (mMTC). Compared with conventional grant-based protocols, grant-free random access skips the exchange of scheduling information to reduce the signaling overhead, and facilitates sharing of access resources to enhance access efficiency. However, some challenges remain to be addressed in the receiver design, such as unknown identity of active users and multi-user interference (MUI) on shared access resources. In this work, we deal with the problem of joint user activity and data detection for grant-free random access. Specifically, the approximate message passing (AMP) algorithm is first employed to mitigate MUI and decouple the signals of different users. Then, we extend the data symbol alphabet to incorporate the null symbols from inactive users. In this way, the joint user activity and data detection problem is formulated as a clustering problem under the Gaussian mixture model. Furthermore, in conjunction with the AMP algorithm, a variational Bayesian inference based clustering (VBIC) algorithm is developed to solve this clustering problem. Simulation results show that, compared with state-of-art solutions, the proposed AMP-combined VBIC (AMP-VBIC) algorithm achieves a significant performance gain in detection accuracy.
Stabilization of Energy-Conserving Gaits for Point-Foot Planar Bipeds
Authors: Aakash Khandelwal, Nilay Kant, Ranjan Mukherjee
Date: 2022-10-26 00:00:00

The problem of designing and stabilizing impact-free, energy-conserving gaits is considered for underactuated, point-foot planar bipeds. Virtual holonomic constraints are used to design energy-conserving gaits. A desired gait corresponds to a periodic hybrid orbit and is stabilized using the Impulse Controlled Poincar\'e Map approach. Numerical simulations for the case of a five-link biped demonstrate convergence to a desired gait from arbitrary initial conditions.
Experimental Flight Testing of a Fault-Tolerant Adaptive Autopilot for Fixed-Wing Aircraft
Authors: Joonghyun Lee, John Spencer, Siyuan Shao, Juan Augusto Paredes, Dennis S. Bernstein, Ankit Goel
Date: 2022-10-24 00:00:00

This paper presents an adaptive autopilot for fixed-wing aircraft and compares its performance with a fixed-gain autopilot. The adaptive autopilot is constructed by augmenting the autopilot architecture with adaptive control laws that are updated using retrospective cost adaptive control. In order to investigate the performance of the adaptive autopilot, the default gains of the fixed-gain autopilot are scaled to degrade its performance. This scenario provides a venue for determining the ability of the adaptive autopilot to compensate for the degraded fixed-gain autopilot. Next, the performance of the adaptive autopilot is examined under failure conditions by simulating a scenario where one of the control surfaces is assumed to be stuck at an unknown angle. The adaptive autopilot is also tested in physical flight experiments under degraded-nominal conditions, and the resulting performance improvement is examined.
Multitask Detection of Speaker Changes, Overlapping Speech and Voice Activity Using wav2vec 2.0
Authors: Marie Kunešová, Zbyněk Zajíc
Date: 2022-10-26 00:00:00
Tasks: Action Detection, Activity Detection, Change Detection, Self-Supervised Learning

Self-supervised learning approaches have lately achieved great success on a broad spectrum of machine learning problems. In the field of speech processing, one of the most successful recent self-supervised models is wav2vec 2.0. In this paper, we explore the effectiveness of this model on three basic speech classification tasks: speaker change detection, overlapped speech detection, and voice activity detection. First, we concentrate on only one task -- speaker change detection -- where our proposed system surpasses the previously reported results on four different corpora, and achieves comparable performance even when trained on out-of-domain data from an artificially designed dataset. Then we expand our approach to tackle all three tasks in a single multitask system with state-of-the-art performance on the AMI corpus. The implementation of the algorithms in this paper is publicly available at https://github.com/mkunes/w2v2_audioFrameClassification.
Mixed-EVC: Mixed Emotion Synthesis and Control in Voice Conversion
Authors: Kun Zhou, Berrak Sisman, Carlos Busso, Bin Ma, Haizhou Li
Date: 2022-10-25 00:00:00
Tasks: Attribute, Voice Conversion

Emotional voice conversion (EVC) traditionally targets the transformation of spoken utterances from one emotional state to another, with previous research mainly focusing on discrete emotion categories. This paper departs from the norm by introducing a novel perspective: a nuanced rendering of mixed emotions and enhancing control over emotional expression. To achieve this, we propose a novel EVC framework, Mixed-EVC, which only leverages discrete emotion training labels. We construct an attribute vector that encodes the relationships among these discrete emotions, which is predicted using a ranking-based support vector machine and then integrated into a sequence-to-sequence (seq2seq) EVC framework. Mixed-EVC not only learns to characterize the input emotional style but also quantifies its relevance to other emotions during training. As a result, users have the ability to assign these attributes to achieve their desired rendering of mixed emotions. Objective and subjective evaluations confirm the effectiveness of our approach in terms of mixed emotion synthesis and control while surpassing traditional baselines in the conversion of discrete emotions from one to another.
Weighted Pressure Matching Based on Kernel Interpolation For Sound Field Reproduction
Authors: Shoichi Koyama, Kazuyuki Arikawa
Date: 2022-10-26 00:00:00

A sound field reproduction method called weighted pressure matching is proposed. Sound field reproduction is aimed at synthesizing the desired sound field using multiple loudspeakers inside a target region. Optimization-based methods are derived from the minimization of errors between synthesized and desired sound fields, which enable the use of an arbitrary array geometry in contrast with integral-equation-based methods. Pressure matching is widely used in the optimization-based sound field reproduction methods because of its simplicity of implementation. Its cost function is defined as the synthesis errors at multiple control points inside the target region; then, the driving signals of the loudspeakers are obtained by solving a least-squares problem. However, in pressure matching, the region between the control points is not taken into consideration. We define the cost function as the regional integration of the synthesis error over the target region. On the basis of the kernel interpolation of the sound field, this cost function is represented as the weighted square error of the synthesized pressures at the control points. Experimental results indicate that the proposed weighted pressure matching outperforms conventional pressure matching.
Xiaoicesing 2: A High-Fidelity Singing Voice Synthesizer Based on Generative Adversarial Network
Authors: Chunhui Wang, Chang Zeng, Xing He
Date: 2022-10-26 00:00:00
Proceeding: Interspeech 2023 8
Tasks: Generative Adversarial Network, Singing Voice Synthesis

XiaoiceSing is a singing voice synthesis (SVS) system that aims at generating 48kHz singing voices. However, the mel-spectrogram generated by it is over-smoothing in middle- and high-frequency areas due to no special design for modeling the details of these parts. In this paper, we propose XiaoiceSing2, which can generate the details of middle- and high-frequency parts to better construct the full-band mel-spectrogram. Specifically, in order to alleviate this problem, XiaoiceSing2 adopts a generative adversarial network (GAN), which consists of a FastSpeech-based generator and a multi-band discriminator. We improve the feed-forward Transformer (FFT) block by adding multiple residual convolutional blocks in parallel with the self-attention block to balance the local and global features. The multi-band discriminator contains three sub-discriminators responsible for low-, middle-, and high-frequency parts of the mel-spectrogram, respectively. Each sub-discriminator is composed of several segment discriminators (SD) and detail discriminators (DD) to distinguish the audio from different aspects. The experiment on our internal 48kHz singing voice dataset shows XiaoiceSing2 significantly improves the quality of the singing voice over XiaoiceSing.
Deep Learning Based Audio-Visual Multi-Speaker DOA Estimation Using Permutation-Free Loss Function
Authors: Qing Wang, Hang Chen, Ya Jiang, Zhe Wang, Yuyang Wang, Jun Du, Chin-Hui Lee
Date: 2022-10-26 00:00:00
Tasks: Active Speaker Detection, Sound Source Localization

In this paper, we propose a deep learning based multi-speaker direction of arrival (DOA) estimation with audio and visual signals by using permutation-free loss function. We first collect a data set for multi-modal sound source localization (SSL) where both audio and visual signals are recorded in real-life home TV scenarios. Then we propose a novel spatial annotation method to produce the ground truth of DOA for each speaker with the video data by transformation between camera coordinate and pixel coordinate according to the pin-hole camera model. With spatial location information served as another input along with acoustic feature, multi-speaker DOA estimation could be solved as a classification task of active speaker detection. Label permutation problem in multi-speaker related tasks will be addressed since the locations of each speaker are used as input. Experiments conducted on both simulated data and real data show that the proposed audio-visual DOA estimation model outperforms audio-only DOA estimation model by a large margin.
Reducing Language confusion for Code-switching Speech Recognition with Token-level Language Diarization
Authors: Hexin Liu, HaiHua Xu, Leibny Paola Garcia, Andy W. H. Khong, Yi He, Sanjeev Khudanpur
Date: 2022-10-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Code-switching (CS) refers to the phenomenon that languages switch within a speech signal and leads to language confusion for automatic speech recognition (ASR). This paper aims to address language confusion for improving CS-ASR from two perspectives: incorporating and disentangling language information. We incorporate language information in the CS-ASR model by dynamically biasing the model with token-level language posteriors which are outputs of a sequence-to-sequence auxiliary language diarization module. In contrast, the disentangling process reduces the difference between languages via adversarial training so as to normalize two languages. We conduct the experiments on the SEAME dataset. Compared to the baseline model, both the joint optimization with LD and the language posterior bias achieve performance improvement. The comparison of the proposed methods indicates that incorporating language information is more effective than disentangling for reducing language confusion in CS speech.
UFO2: A unified pre-training framework for online and offline speech recognition
Authors: Li Fu, Siqi Li, Qingtao Li, Liping Deng, Fangzhu Li, Lu Fan, Meng Chen, Xiaodong He
Date: 2022-10-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Self-Supervised Learning, speech-recognition, Speech Recognition

In this paper, we propose a Unified pre-training Framework for Online and Offline (UFO2) Automatic Speech Recognition (ASR), which 1) simplifies the two separate training workflows for online and offline modes into one process, and 2) improves the Word Error Rate (WER) performance with limited utterance annotating. Specifically, we extend the conventional offline-mode Self-Supervised Learning (SSL)-based ASR approach to a unified manner, where the model training is conditioned on both the full-context and dynamic-chunked inputs. To enhance the pre-trained representation model, stop-gradient operation is applied to decouple the online-mode objectives to the quantizer. Moreover, in both the pre-training and the downstream fine-tuning stages, joint losses are proposed to train the unified model with full-weight sharing for the two modes. Experimental results on the LibriSpeech dataset show that UFO2 outperforms the SSL-based baseline method by 29.7% and 18.2% relative WER reduction in offline and online modes, respectively.
Effect of different splitting criteria on the performance of speech emotion recognition
Authors: Bagus Tris Atmaja, Akira Sasou
Date: 2022-10-26 00:00:00
Tasks: Emotion Recognition, Sentence, Speech Emotion Recognition

Traditional speech emotion recognition (SER) evaluations have been performed merely on a speaker-independent condition; some of them even did not evaluate their result on this condition. This paper highlights the importance of splitting training and test data for SER by script, known as sentence-open or text-independent criteria. The results show that employing sentence-open criteria degraded the performance of SER. This finding implies the difficulties of recognizing emotion from speech in different linguistic information embedded in acoustic information. Surprisingly, text-independent criteria consistently performed worse than speaker+text-independent criteria. The full order of difficulties for splitting criteria on SER performances from the most difficult to the easiest is text-independent, speaker+text-independent, speaker-independent, and speaker+text-dependent. The gap between speaker+text-independent and text-independent was smaller than other criteria, strengthening the difficulties of recognizing emotion from speech in different sentences.
Sinusoidal Frequency Estimation by Gradient Descent
Authors: Ben Hayes, Charalampos Saitis, György Fazekas
Date: 2022-10-26 00:00:00
Tasks: parameter estimation, Time Series, Time Series Analysis, Time Series Forecasting

Sinusoidal parameter estimation is a fundamental task in applications from spectral analysis to time-series forecasting. Estimating the sinusoidal frequency parameter by gradient descent is, however, often impossible as the error function is non-convex and densely populated with local minima. The growing family of differentiable signal processing methods has therefore been unable to tune the frequency of oscillatory components, preventing their use in a broad range of applications. This work presents a technique for joint sinusoidal frequency and amplitude estimation using the Wirtinger derivatives of a complex exponential surrogate and any first order gradient-based optimizer, enabling end to-end training of neural network controllers for unconstrained sinusoidal models.
Enhanced Fuzzy Decomposition of Sound Into Sines, Transients, and Noise
Authors: Leonardo Fierro, Vesa Välimäki
Date: 2022-10-25 00:00:00

The decomposition of sounds into sines, transients, and noise is a long-standing research problem in audio processing. The current solutions for this three-way separation detect either horizontal and vertical structures or anisotropy and orientations in the spectrogram to identify the properties of each spectral bin and classify it as sinusoidal, transient, or noise. This paper proposes an enhanced three-way decomposition method based on fuzzy logic, enabling soft masking while preserving the perfect reconstruction property. The proposed method allows each spectral bin to simultaneously belong to two classes, sine and noise or transient and noise. Results of a subjective listening test against three other techniques are reported, showing that the proposed decomposition yields a better or comparable quality. The main improvement appears in transient separation, which enjoys little or no loss of energy or leakage from the other components and performs well for test signals presenting strong transients. The audio quality of the separation is shown to depend on the complexity of the input signal for all tested methods. The proposed method helps improve the quality of various audio processing applications. A successful implementation over a state-of-the-art time-scale modification method is reported as an example.
Disentangled Speech Representation Learning for One-Shot Cross-lingual Voice Conversion Using $β$-VAE
Authors: Hui Lu, Disong Wang, Xixin Wu, Zhiyong Wu, Xunying Liu, Helen Meng
Date: 2022-10-25 00:00:00
Tasks: Disentanglement, Representation Learning, Speech Representation Learning, Voice Conversion

We propose an unsupervised learning method to disentangle speech into content representation and speaker identity representation. We apply this method to the challenging one-shot cross-lingual voice conversion task to demonstrate the effectiveness of the disentanglement. Inspired by $\beta$-VAE, we introduce a learning objective that balances between the information captured by the content and speaker representations. In addition, the inductive biases from the architectural design and the training dataset further encourage the desired disentanglement. Both objective and subjective evaluations show the effectiveness of the proposed method in speech disentanglement and in one-shot cross-lingual voice conversion.
Streaming Parrotron for on-device speech-to-speech conversion
Authors: Oleg Rybakov, Fadi Biadsy, Xia Zhang, Liyang Jiang, Phoenix Meadowlark, Shivani Agrawal
Date: 2022-10-25 00:00:00
Tasks: CPU, Decoder, Quantization, STS

We present a fully on-device streaming Speech2Speech conversion model that normalizes a given input speech directly to synthesized output speech. Deploying such a model on mobile devices pose significant challenges in terms of memory footprint and computation requirements. We present a streaming-based approach to produce an acceptable delay, with minimal loss in speech conversion quality, when compared to a reference state of the art non-streaming approach. Our method consists of first streaming the encoder in real time while the speaker is speaking. Then, as soon as the speaker stops speaking, we run the spectrogram decoder in streaming mode along the side of a streaming vocoder to generate output speech. To achieve an acceptable delay-quality trade-off, we propose a novel hybrid approach for look-ahead in the encoder which combines a look-ahead feature stacker with a look-ahead self-attention. We show that our streaming approach is almost 2x faster than real time on the Pixel4 CPU.
Simulation-based Modelling of Growth and Pollination of Greenhouse Strawberry
Authors: Zhihao Cao, Hongchun Qu
Date: 2022-10-26 00:00:00

The cultivated strawberry Fragaria ananassa Duch. is widely planted in greenhouses in China. Its production heavily depends on pollination services. Compared with artificial pollination, bee pollination can significantly improve fruit quality and save considerable labor requirement. Multiple factors such as bee foraging behavior, planting pattern and the spatial complexity of the greenhouse environment interacting over time and space are major obstacles to understanding of bee pollination dynamics. We propose a spatially-explicit agent-based simulation model which allows users to explore how various factors including bee foraging behavior and strawberry phenology conditions as well as the greenhouse environment influence pollination efficiency and fruit quality. Simulation experiments allowed us to compare pollination efficiencies in different conditions. Especially, the cause of bee pollination advantage, optimal bee density and bee hive location were discussed based on sensitivity analysis. In addition, simulation results provide some insights for strawberry planting in a greenhouse. The firmly validated open-source model is a useful tool for hypothesis testing and theory development for strawberry pollination research.
A Control-theoretic Model for Bidirectional Molecular Communication Systems
Authors: Taishi Kotsuka, Yutaka Hori
Date: 2022-10-26 00:00:00

Molecular communication (MC) enables cooperation of spatially dispersed molecular robots through the feedback control mediated by diffusing signal molecules. However, conventional analysis frameworks for the MC channels mostly consider the dynamics of unidirectional communication, lacking the effect of feedback interactions. In this paper, we propose a general control-theoretic modeling framework for bidirectional MC systems capable of capturing the dynamics of feedback control via MC in a systematic manner. The proposed framework considers not only the dynamics of molecular diffusion but also the boundary dynamics at the molecular robots that captures the lag due to the molecular transmission/reception process affecting the performance of the entire feedback system. Thus, methods in control theory can be applied to systematically analyze various dynamical properties of the feedback system. We perform a frequency response analysis based on the proposed framework to show a general design guideline for MC channels to transfer signal with desired control bandwidth. Finally, these results are demonstrated by showing the step-by-step design procedure of a specific MC channel satisfying a given specification.
Duality-Based Stochastic Policy Optimization for Estimation with Unknown Noise Covariances
Authors: Shahriar Talebi, Amirhossein Taghvaei, Mehran Mesbahi
Date: 2022-10-26 00:00:00

Duality of control and estimation allows mapping recent advances in data-guided control to the estimation setup. This paper formalizes and utilizes such a mapping to consider learning the optimal (steady-state) Kalman gain when process and measurement noise statistics are unknown. Specifically, building on the duality between synthesizing optimal control and estimation gains, the filter design problem is formalized as direct policy learning. In this direction, the duality is used to extend existing theoretical guarantees of direct policy updates for Linear Quadratic Regulator (LQR) to establish global convergence of the Gradient Descent (GD) algorithm for the estimation problem--while addressing subtle differences between the two synthesis problems. Subsequently, a Stochastic Gradient Descent (SGD) approach is adopted to learn the optimal Kalman gain without the knowledge of noise covariances. The results are illustrated via several numerical examples.
Recursive Secondary Controller for Voltage Profile Improvement Based on Primary Virtual Admittance Control
Authors: Dionysios Moutevelis, Javier Roldan-Perez, Njegos Jankovic, Milan Prodanovic
Date: 2022-10-26 00:00:00

This paper proposes a recursive, virtual admittance based, secondary controller for DG units that improves the voltage profile in distribution networks. First, the adaptation of the virtual admittance concept for the goal of voltage regulation is explained. Then, a recursive secondary controller is developed to periodically update the virtual admittance gains. The controller is formulated as an optimization problem with current and stability limitations as constraints. Measurements across the grid, transmitted through low-bandwidth communications, are used to simplify the calculations, resulting in a recursive algorithm. Weight vectors are included in the objective function to allow participation flexibility of each converter. Results show that the primary virtual admittance controller is able to mitigate over- and under-voltages in steady state and under transient conditions. Subsequently, the secondary controller is shown to further improve the voltage profiles across the grid. Experimental results obtained from a laboratory environment, comprising three DG units and a grid emulator, validate the functionality of the complete control structure.