30 accepted papers
Neural audio codec (NAC) is essential for reconstructing high-quality speech signals and generating discrete representations for downstream speech language models. However, ensuring accurate semantic modeling while maintaining high-fidelity reconstruction under ultra-low bitrate constraints remains…
We propose Federated Preconditioned Mixing (FedPM), a novel Federated Learning (FL) method that leverages second-order optimization. Prior methods - such as LocalNewton, LTDA, and FedSophia - have incorporated second-order optimization in FL by performing iterative local updates on clients and apply
Language-queried audio source separation (LASS) is an emerging research field that has recently received increasing attention. This task aims to isolate individual sources from a mixture of signals using natural language descriptions, enabling applications in various areas such as automatic audio ed…
Sound Event Localization and Detection (SELD) is the combined task of detecting sound events and estimating their spatial locations. We propose a Sound source Distance Estimation (SDE) method for SELD that utilizes a physics-informed prior. The conventional data-driven approach of SDE for SELD can h…
Sound Event Localization and Detection (SELD) is the task of estimating the class, duration, and direction of arrival (DOA) of sound events. State-of-the-art SELD systems use a data-driven approach based on Deep Neural Networks (DNNs) to deal with complex situations involving overlapping and moving…
The 3GPP IVAS codec specifies an EVS-compatible stereo downmix as one of the key functionalities. This paper describes how this novel active downmix scheme has been devised to achieve high and stable quality from stereo input to EVS encoder/decoder with no additional algorithmic delay. An example of…
We aim to perform sound event localization and detection (SELD) using wearable equipment for a moving human, such as a pedestrian. Conventional SELD tasks have dealt only with microphone arrays located in static positions. However, self-motion with three rotational and three translational degrees of…
With the rapid development of neural networks in recent years, the ability of various networks to enhance the magnitude spectrum of noisy speech in the single-channel speech enhancement domain has become exceptionally outstanding. However, enhancing the phase spectrum using neural networks is often…
Masked Autoencoders is a simple yet powerful self-supervised learning method. However, it learns representations indirectly by reconstructing masked input patches. Several methods learn representations directly by predicting representations of masked patches; however, we think using all patches to e…
We tackle a challenging task: multi-view and multi-modal event detection that detects events in a wide-range real environment by utilizing data from distributed cameras and microphones and their weak labels. In this task, distributed sensors are utilized complementarily to capture events that are di…
This paper proposes formulating declipping as a constrained multiple measurement vector (MMV) optimization problem that has a ${\ell _{2,0}}$ group norm as its cost function for further improving the state-of-the-art declipping method SParse Audio DEclipper (SPADE). This paper shows that the MMV opt…
A novel asynchronous decentralized optimization method that follows Stochastic Variance Reduction (SVR) is proposed. Average consensus algorithms, such as Decentralized Stochastic Gradient Descent (DSGD), facilitate distributed training of machine learning models. However, the gradient will drift wi…
We propose a frequency-domain blind source separation method that uses (a) the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm of orthonormal vectors of estimated source signals as a sparsity measure and (b) Cayley transform for optimizin…
We propose an end-to-end speech enhancement method with trainable time-frequency (T-F) transform based on invertible deep neural network (DNN). The resent development of speech enhancement is brought by using DNN. The ordinary DNN-based speech enhancement employs T-F transform, typically the short-t…
Phase reconstruction, which estimates phase from a given amplitude spectrogram, is an active research field in acoustical signal processing with many applications including audio synthesis. To take advantage of rich knowledge from data, several studies presented deep neural network (DNN)–based phase…
We propose a speech enhancement method using a causal deep neural network (DNN) for real-time applications. DNN has been widely used for estimating a time-frequency (T-F) mask which enhances a speech signal. One popular DNN structure for that is a recurrent neural network (RNN) owing to its capabili…
We propose a similarity function for one-shot anomaly detection in sounds (ADS) called SPecific anomaly IDentifiER network (SPIDERnet). In ADS systems, since overlooking an anomaly may result in serious incidents, we need to update such systems using an (often only one) overlooked anomalous sample.…
Previous reports have mentioned the possibility that subjective quality of the echo-suppressed speech signal can be estimated based on perceptual evaluation of speech quality (PESQ), but there are few experimental results. We propose third-party listening and conversational test procedures to assess…
Supervised anomaly detection has been a tough problem due to its necessity of special handling of unseen anomalies. In this paper, we present a heuristic implementation of variational auto-encoder with von-Mises Fisher prior applied to a supervised anomaly detector. The closed latent space like sphe…
We tackle unsupervised anomaly detection (UAD), a problem of detecting data that significantly differ from normal data. UAD is typically solved by using density estimation. Recently, deep neural network (DNN)-based density estimators, such as Normalizing Flows, have been attracting attention. Howeve…
We propose a data-driven design method of perfect-reconstruction filterbank (PRFB) for sound-source enhancement (SSE) based on deep neural network (DNN). DNNs have been used to estimate a time-frequency (T-F) mask in the short-time Fourier transform (STFT) domain. Their training is more stable when…
This paper presents a novel phase reconstruction method (only from a given amplitude spectrogram) by combining a signal-processing-based approach and a deep neural network (DNN). To retrieve a time-domain signal from its amplitude spectrogram, the corresponding phase is required. One of the popular…
We propose a novel beamformer design method that exploits probabilistic assumptions on auxiliary variables derived from filters and observed signals. Many conventional beamformer design methods can be understood in the context of optimization problems for some probabilistic cost functions. However,…
A non-negative matrix factorization (NMF) algorithm based on Bregman monotone operator splitting (B-MOS) is proposed. Several commonly used NMF algorithms, such as the multiplicative update method, are often used in source separation for speech and image signals. To improve the convergence rate in t…
In anomaly detection systems, overlooking anomalies may result in serious incidents. Thus, when a system overlooks an anomaly, we need to update the system to never overlook the observed type of anomalies twice. There are roughly two possible approaches to solve this problem; re-training the whole s…
This study proposes a trainable adaptive window switching (AWS) method and apply it to a deep-neural-network (DNN) for speech enhancement in the modified discrete cosine transform domain. Time-frequency (T-F) mask processing in the short-time Fourier transform (STFT)-domain is a typical speech enhan…
Anomalies have broad patterns corresponding to their causes. In industry, anomalies are typically observed as equipment failures. Anomaly detection aims to detect such failures as anomalies. Although this is usually a binary classification task, the potential existence of unseen (unknown) failures m…
This paper presents an end-to-end deep neural network (DNN)-based source enhancement on the basis of a time-frequency (T-F) mask processing in the modified discrete cosine transform (MDCT)-domain. To retrieve the target signal perfectly in the discrete Fourier transform (DFT)-domain, both amplitude…
We propose a method to estimate a reverberant sound field by compressed sensing approach without using the hyper parameter for controlling sparsity. This method first applies sparse iterative covariance-based approach (SPICE) to the power spectral density matrix of the microphone signals and obtains…
A new codec for Enhanced Voice Services (EVS), the successor of the current mobile HD voice codec AMR-WB, was standardized by the 3rd Generation Partnership Project (3GPP) in September 2014. The EVS codec addresses 3GPP's needs for cutting-edge technology enabling operation of 3GPP mobile communicat…