pwc-archive/papers-with-abstracts huggingface dataset:
Multi-task Learning with Metadata for Music Mood Classification
Authors: Rajnish Kumar, Manjeet Dahiya
Date: 2021-10-10 00:00:00
Tasks: Classification, Multi-Task Learning

Mood recognition is an important problem in music informatics and has key applications in music discovery and recommendation. These applications have become even more relevant with the rise of music streaming. Our work investigates the research question of whether we can leverage audio metadata such as artist and year, which is readily available, to improve the performance of mood classification models. To this end, we propose a multi-task learning approach in which a shared model is simultaneously trained for mood and metadata prediction tasks with the goal to learn richer representations. Experimentally, we demonstrate that applying our technique on the existing state-of-the-art convolutional neural networks for mood classification improves their performances consistently. We conduct experiments on multiple datasets and report that our approach can lead to improvements in the average precision metric by up to 8.7 points.
Poformer: A simple pooling transformer for speaker verification
Authors: Yufeng Ma, Yiwei Ding, Miao Zhao, Yu Zheng, Min Liu, Minqiang Xu
Date: 2021-10-10 00:00:00
Tasks: Speaker Verification

Most recent speaker verification systems are based on extracting speaker embeddings using a deep neural network. The pooling layer in the network aims to aggregate frame-level features extracted by the backbone. In this paper, we propose a new transformer based pooling structure called PoFormer to enhance the ability of the pooling layer to capture information along the whole time axis. Different from previous works that apply attention mechanism in a simple way or implement the multi-head mechanism in serial instead of in parallel, PoFormer follows the initial transformer structure with some minor modifications like a positional encoding generator, drop path and LayerScale to make the training procedure more stable and to prevent overfitting. Evaluated on various datasets, PoFormer outperforms the existing pooling system with at least a 13.00% improvement in EER and a 9.12% improvement in minDCF.
Streaming on-device detection of device directed speech from voice and touch-based invocation
Authors: Ognjen Rudovic, Akanksha Bindal, Vineet Garg, Pramod Simha, Pranay Dighe, Sachin Kajarekar
Date: 2021-10-09 00:00:00
Tasks: Computational Efficiency

When interacting with smart devices such as mobile phones or wearables, the user typically invokes a virtual assistant (VA) by saying a keyword or by pressing a button on the device. However, in many cases, the VA can accidentally be invoked by the keyword-like speech or accidental button press, which may have implications on user experience and privacy. To this end, we propose an acoustic false-trigger-mitigation (FTM) approach for on-device device-directed speech detection that simultaneously handles the voice-trigger and touch-based invocation. To facilitate the model deployment on-device, we introduce a new streaming decision layer, derived using the notion of temporal convolutional networks (TCN) [1], known for their computational efficiency. To the best of our knowledge, this is the first approach that can detect device-directed speech from more than one invocation type in a streaming fashion. We compare this approach with streaming alternatives based on vanilla Average layer, and canonical LSTMs, and show: (i) that all the models show only a small degradation in accuracy compared with the invocation-specific models, and (ii) that the newly introduced streaming TCN consistently performs better or comparable with the alternatives, while mitigating device undirected speech faster in time, and with (relative) reduction in runtime peak-memory over the LSTM-based approach of 33% vs. 7%, when compared to a non-streaming counterpart.
Universal Paralinguistic Speech Representations Using Self-Supervised Conformers
Authors: Joel Shor, Aren Jansen, Wei Han, Daniel Park, Yu Zhang
Date: 2021-10-09 00:00:00

Many speech applications require understanding aspects beyond the words being spoken, such as recognizing emotion, detecting whether the speaker is wearing a mask, or distinguishing real from synthetic speech. In this work, we introduce a new state-of-the-art paralinguistic representation derived from large-scale, fully self-supervised training of a 600M+ parameter Conformer-based architecture. We benchmark on a diverse set of speech tasks and demonstrate that simple linear classifiers trained on top of our time-averaged representation outperform nearly all previous results, in some cases by large margins. Our analyses of context-window size demonstrate that, surprisingly, 2 second context-windows achieve 96\% the performance of the Conformers that use the full long-term context on 7 out of 9 tasks. Furthermore, while the best per-task representations are extracted internally in the network, stable performance across several layers allows a single universal representation to reach near optimal performance on all tasks.
An evaluation of data augmentation methods for sound scene geotagging
Authors: Helen L. Bear, Veronica Morfi, Emmanouil Benetos
Date: 2021-10-09 00:00:00
Tasks: Acoustic Scene Classification, Classification, Data Augmentation, Scene Classification

Sound scene geotagging is a new topic of research which has evolved from acoustic scene classification. It is motivated by the idea of audio surveillance. Not content with only describing a scene in a recording, a machine which can locate where the recording was captured would be of use to many. In this paper we explore a series of common audio data augmentation methods to evaluate which best improves the accuracy of audio geotagging classifiers. Our work improves on the state-of-the-art city geotagging method by 23% in terms of classification accuracy.
Data Augmentation with Locally-time Reversed Speech for Automatic Speech Recognition
Authors: Si-Ioi Ng, Tan Lee
Date: 2021-10-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, speech-recognition, Speech Recognition

Psychoacoustic studies have shown that locally-time reversed (LTR) speech, i.e., signal samples time-reversed within a short segment, can be accurately recognised by human listeners. This study addresses the question of how well a state-of-the-art automatic speech recognition (ASR) system would perform on LTR speech. The underlying objective is to explore the feasibility of deploying LTR speech in the training of end-to-end (E2E) ASR models, as an attempt to data augmentation for improving the recognition performance. The investigation starts with experiments to understand the effect of LTR speech on general-purpose ASR. LTR speech with reversed segment duration of 5 ms - 50 ms is rendered and evaluated. For ASR training data augmentation with LTR speech, training sets are created by combining natural speech with different partitions of LTR speech. The efficacy of data augmentation is confirmed by ASR results on speech corpora in various languages and speaking styles. ASR on LTR speech with reversed segment duration of 15 ms - 30 ms is found to have lower error rate than with other segment duration. Data augmentation with these LTR speech achieves satisfactory and consistent improvement on ASR performance.
TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context
Authors: Nithin Rao Koluguri, Taejin Park, Boris Ginsburg
Date: 2021-10-08 00:00:00
Tasks: speaker-diarization, Speaker Diarization, Speaker Verification

In this paper, we propose TitaNet, a novel neural network architecture for extracting speaker representations. We employ 1D depth-wise separable convolutions with Squeeze-and-Excitation (SE) layers with global context followed by channel attention based statistics pooling layer to map variable-length utterances to a fixed-length embedding (t-vector). TitaNet is a scalable architecture and achieves state-of-the-art performance on speaker verification task with an equal error rate (EER) of 0.68% on the VoxCeleb1 trial file and also on speaker diarization tasks with diarization error rate (DER) of 1.73% on AMI-MixHeadset, 1.99% on AMI-Lapel and 1.11% on CH109. Furthermore, we investigate various sizes of TitaNet and present a light TitaNet-S model with only 6M parameters that achieve near state-of-the-art results in diarization tasks.
Individualized Hear-through For Acoustic Transparency Using PCA-Based Sound Pressure Estimation At The Eardrum
Authors: Wenyu Jin, Tim Schoof, Henning Schepker
Date: 2021-10-08 00:00:00

The hear-through functionality on hearing devices, which allows hearing equivalent to the open-ear while providing the possibility to modify the sound pressure at the eardrum in a desired manner, has drawn great attention from researchers in recent years. To this end, the output of the device is processed by means of an equalization filter, such that the transfer function between external sound sources and the eardrum is equivalent for the open-ear and the aided condition with the device in the ear. To achieve an ideal performance, the equalization filter design assumes the exact knowledge of all the relevant acoustic transfer functions. A particular challenge is the transfer function between the hearing device receiver and the eardrum, which is difficult to obtain in practice as it requires additional probe-tube measurements. In this work, we address this issue by proposing an individualized hear-through equalization filter design that leverages the measurement of the so-called secondary path to predict the sound pressure at the eardrum. Experimental results using real-ear measured transfer functions confirm that the proposed method achieves a good sound quality compared to the open-ear while outperforming filter designs that do not leverage the proposed estimator.
MusicNet: Compact Convolutional Neural Network for Real-time Background Music Detection
Authors: Chandan K. A. Reddy, Vishak Gopa, Harishchandra Dubey, Sergiy Matusevych, Ross Cutler, Robert Aichner
Date: 2021-10-08 00:00:00

With the recent growth of remote work, online meetings often encounter challenging audio contexts such as background noise, music, and echo. Accurate real-time detection of music events can help to improve the user experience. In this paper, we present MusicNet, a compact neural model for detecting background music in the real-time communications pipeline. In video meetings, music frequently co-occurs with speech and background noises, making the accurate classification quite challenging. We propose a compact convolutional neural network core preceded by an in-model featurization layer. MusicNet takes 9 seconds of raw audio as input and does not require any model-specific featurization in the product stack. We train our model on the balanced subset of the Audio Set~\cite{gemmeke2017audio} data and validate it on 1000 crowd-sourced real test clips. Finally, we compare MusicNet performance with 20 state-of-the-art models. MusicNet has a true positive rate (TPR) of 81.3% at a 0.1% false positive rate (FPR), which is significantly better than state-of-the-art models included in our study. MusicNet is also 10x smaller and has 4x faster inference than the best performing models we benchmarked.
Local Effects of Grid-Forming Converters Providing Frequency Regulation to Bulk Power Grids
Authors: Antonio Zecchino, Francesco Gerini, Yihui Zuo, Rachid Cherkaoui, Mario Paolone, Elena Vagnoni
Date: 2021-10-11 00:00:00

The progressive displacing of conventional generation in favour of renewable energy sources requires restoring an adequate capacity of regulating power to ensure reliable operation of power systems. Battery Energy Storage Systems (BESSs) are considered to be promising assets to restore suitable frequency regulation capacity levels. BESSs are typically connected to the grid with power-converters, able to operate in either grid-forming or grid-following modes. This paper quantitatively assesses the impact on the local distribution grid of BESSs providing frequency regulation to bulk power systems. Specific metrics are proposed to compare the performance of grid-forming and grid-following control. Experimental results are obtained taking advantage of a 720 kVA/500 kWh BESS connected to the 20 kV distribution grid of the EPFL campus. The quantitative evaluation based on suitably proposed metrics confirms the superior performance of the grid-forming strategy, compared to the grid-following one.
Edge-wise funnel output synchronization of heterogeneous agents with relative degree one
Authors: Jin Gyu Lee, Thomas Berger, Stephan Trenn, Hyungbo Shim
Date: 2021-10-11 00:00:00

When a group of heterogeneous node dynamics are diffusively coupled with a high coupling gain, the group exhibits a collective emergent behavior which is governed by a simple algebraic average of the node dynamics called the blended dynamics. This finding has been utilized for designing heterogeneous multi-agent systems by building the desired blended dynamics first and then splitting it into the node dynamics. However, to compute the magnitude of the coupling gain, each agent needs to know global information such as the number of participating nodes, the graph structure, and so on, which prevents a fully decentralized design of the node dynamics in conjunction with the coupling laws. To resolve this issue, the idea of funnel control, which is a method for adaptive gain selection, can be exploited for a node-wise coupling, but the price to pay is that the collective emergent behavior is no longer governed by a simple average of the node dynamics. Our analysis reveals that this drawback can be avoided by an edge-wise design premise, which is the idea that we present in this paper. After all, we gain benefits such as a fully decentralized design without global information, collective emergent behavior being governed by the blended dynamics, and the plug-and-play operation based on edge-wise handshaking between two nodes.
The Effects of Vehicle-to-Infrastructure Communication Reliability on Performance of Signalized Intersection Traffic Control
Authors: Ilya Finkelberg, Tibor Petrov, Ayelet Gal-Tzur, Nina Zarkhin, Peter Pocta, Tatiana Kovacikova, Lubos Buzna, Milan Dado, Tomer Toledo
Date: 2021-10-11 00:00:00

A vehicle-to-infrastructure communication can inform an intersection controller about the location and speed of connected vehicles. Recently, the design of adaptive intersection control algorithms that take advantage of this information evoked a lot of research, typically assuming a perfect communication. In this study, we explore possible effects of a temporal decrease in the reliability of the communication channel, on a throughput of a signalized intersection. We model road traffic and DSRC-VANET communication by integrating the well-established traffic and communication simulation tools (Vissim and OMNeT++). Comparisons of the perfect communication conditions with challenging, but realistic conditions involving communication distortions show that the adaptive intersection control may suffer from significantly increased average delays of vehicles. The level of delays is largely independent of whether the communication distortions affect all or only a single intersection approach. For some signal groups, the average vehicle delays are significantly increased, while for the others they are decreased, leading to the disbalance and unfairness. We show that the data received in the previous time intervals and simple assumptions about the vehicle movements can be used to estimate the lost data. The compensation of the communication distortions affecting a single intersection approach decreases the average vehicle delays. When the communication distortions impacting all the intersection approaches are compensated for, the vehicle delays are even set back to the levels comparable with the perfect communication conditions. Overall, the results demonstrate that the impact of the communication distortions should be considered in the design of the adaptive intersection control algorithms.
Tracking of stabilizing, optimal control in fixed-time based on time-varying objective function
Authors: Patrick Schmidt, Thomas Göhrt, Stefan Streif
Date: 2021-10-11 00:00:00

The controller of an input-affine system is determined through minimizing a time-varying objective function, where stabilization is ensured via a Lyapunov function decay condition as constraint. This constraint is incorporated into the objective function via a barrier function. The time-varying minimum of the resulting relaxed cost function is determined by a tracking system. This system is constructed using derivatives up to second order of the relaxed cost function and improves the existing approaches in time-varying optimization. Under some mild assumptions, the tracking system yields a solution which is feasible for all times, and it converges to the optimal solution of the relaxed objective function in a user-defined fixed-time. The effectiveness of these results in comparison to exponential convergence is demonstrated in a case study.
Service Scheduling for Random Requests with Fixed Waiting Costs
Authors: Ramya Burra, Chandramani Singh, Joy Kuri
Date: 2021-10-11 00:00:00
Tasks: Scheduling

We study service scheduling problems in a slotted system in which agents arrive with service requests according to a Bernoulli process and have to leave within two slots after arrival, service costs are quadratic in service rates, and there are also waiting costs. We consider fixed waiting costs. We frame the problems as average cost Markov decision processes. While the studied system is a linear system with quadratic costs, it has state dependent control. Moreover, it also possesses a non-standard cost function structure in the case of fixed waiting costs, rendering the optimization problem complex. Here, we characterize optimal policy. We also consider a system in which the agents make scheduling decisions for their respective service requests keeping their own cost in view. We again consider fixed waiting costs and frame this scheduling problem as a stochastic game. Here, we provide Nash equilibrium.
State Estimation Using a Network of Distributed Observers With Unknown Inputs
Authors: Guitao Yang, Angelo Barboni, Hamed Rezaee, Thomas Parisini
Date: 2021-10-11 00:00:00
Tasks: State Estimation

State estimation for a class of linear time-invariant systems with distributed output measurements (distributed sensors) and unknown inputs is addressed in this paper. The objective is to design a network of observers such that the state vector of the entire system can be estimated, while each observer (or node) has access to only local output measurements that may not be sufficient on its own to reconstruct the entire system's state. Existing results in the literature on distributed state estimation assume either that the system does not have inputs, or that all the system's inputs are globally known to all the observers. Accordingly, we address this gap by proposing a distributed observer capable of estimating the overall system's state in the presence of inputs, while each node only has limited local information on inputs and outputs. We provide a design method that guarantees convergence of the estimation errors under some mild joint detectability conditions. This design suits undirected communication graphs that may have switching topologies and also applies to strongly connected directed graphs. We also give existence conditions that harmonize with existing results on unknown input observers. Finally, simulation results verify the effectiveness of the proposed estimation scheme for various scenarios.
Internal Feedback in Biological Control: Architectures and Examples
Authors: Anish A. Sarma, Jing Shuang Li, Josefin Stenberg, Gwyneth Card, Elizabeth S. Heckscher, Narayanan Kasthuri, Terrence Sejnowski, John C. Doyle
Date: 2021-10-11 00:00:00
Tasks: Diversity

Feedback is ubiquitous in both biological and engineered control systems. In biology, in addition to typical feedback between plant and controller, we observe feedback pathways within control systems, which we call internal feedback pathways (IFPs), that are often very complex. IFPs are most familiar in neural systems, our primary motivation, but they appear everywhere from bacterial signal transduction to the human immune system. In this paper, we describe these very different motivating examples and introduce the concepts necessary to explain their complex IFPs, particularly the severe speed-accuracy tradeoffs that constrain the hardware in biology. We also sketch some minimal theory for extremely simplified toy models that nevertheless highlight the importance of diversity-enabled sweet spots (DESS) in mitigating the impact of hardware tradeoffs. For more realistic models, standard modern and robust control theory can give some insights into previously cryptic IFPs, and the new System Level Synthesis theory expands this substantially. These additional theories explaining IFPs will be explored in more detail in several companion papers.
Understanding the Safety Requirements for Learning-based Power Systems Operations
Authors: Yize Chen, Daniel Arnold, Yuanyuan Shi, Sean Peisert
Date: 2021-10-11 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, energy management, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent advancements in machine learning and reinforcement learning have brought increased attention to their applicability in a range of decision-making tasks in the operations of power systems, such as short-term emergency control, Volt/VAr control, long-term residential demand response and battery energy management. Despite the promises of providing strong representation of complex system dynamics and fast, efficient learned operation strategies, the safety requirements of such learning paradigms are less discussed. This paper explores the design requirements on both data and model side of such learning algorithms by exploiting the impacts of adversarial attacks on safety critical system operations. Case studies performed on both voltage regulation and topology control tasks demonstrated the potential vulnerabilities of the standard reinforcement learning algorithms, and possible measures of machine learning robustness and security are discussed for power systems operation tasks.