Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music speech emotion classification sound event detection. Recently, neural networks have been applied to tackle problems. However, previous systems are built on specific datasets with limited durations. computer vision natural language processing, pretrained large-scale generalized well tasks. there pretraining for...

Scientists spend an increasing amount of time building and using software. However, most scientists are never taught how to do this efficiently. As a result, many unaware tools practices that would allow them write more reliable maintainable code with less effort. We describe set best for scientific software development have solid foundations in research experience, improve scientists' productivity the reliability their

For intelligent systems to make best use of the audio modality, it is important that they can recognize not just speech and music, which have been researched as specific tasks, but also general sounds in everyday environments. To stimulate research this field we conducted a public challenge: IEEE Audio Acoustic Signal Processing Technical Committee challenge on Detection Classification Scenes Events (DCASE). In paper, report state art automatically classifying scenes, detecting events. We...

In this article we present an account of the state-of-the-art in acoustic scene classification (ASC), task classifying environments from sounds they produce. Starting a historical review previous research area, define general framework for ASC and different imple- mentations its components. We then describe range algorithms submitted data challenge that was held to provide fair benchmark techniques. The dataset recorded purpose is presented, along with performance metrics are used evaluate...

Public evaluation campaigns and datasets promote active development in target research areas, allowing direct comparison of algorithms. The second edition the challenge on detection classification acoustic scenes events (DCASE 2016) has offered such an opportunity for state-of-the-art methods, succeeded drawing together a large number participants from academic industrial backgrounds. In this paper, we report tasks outcomes DCASE 2016 challenge. comprised four tasks: scene classification,...

Automatic species classification of birds from their sound is a computational tool increasing importance in ecology, conservation monitoring and vocal communication studies. To make useful practice, it crucial to improve its accuracy while ensuring that can run at big data scales. Many approaches use acoustic measures based on spectrogram-type data, such as the Mel-frequency cepstral coefficient (MFCC) features which represent manually-designed summary spectral information. However, recent...

In this paper, we present a gated convolutional neural network and temporal attention-based localization method for audio classification, which won the 1st place in large-scale weakly supervised sound event detection task of Detection Classification Acoustic Scenes Events (DCASE) 2017 challenge. The clips task, are extracted from YouTube videos, manually labelled with one or more tags, but without time stamps events, hence referred to as data. Two subtasks defined challenge including tagging...

Text-to-audio (TTA) system has recently gained attention for its ability to synthesize general audio based on text descriptions. However, previous studies in TTA have limited generation quality with high computational costs. In this study, we propose AudioLDM, a that is built latent space learn the continuous representations from contrastive language-audio pretraining (CLAP) latents. The pretrained CLAP models enable us train LDMs embedding while providing as condition during sampling. By...

The advancement of audio-language (AL) multimodal learning tasks has been significant in recent years, yet the limited size existing datasets poses challenges for researchers due to costly and time-consuming collection process.To address this data scarcity issue, we introduce WavCaps, first large-scale weakly-labelled audio captioning dataset, comprising approximately 400k clips with paired captions.We sourced their raw descriptions from web sources a sound event detection dataset.However,...

Although audio generation shares commonalities across different types of audio, such as speech, music, and sound effects, designing models for each type requires careful consideration specific objectives biases that can significantly differ from those other types. To bring us closer to a unified perspective generation, this paper proposes holistic framework utilizes the same learning method effect generation. Our general representation called "language audio" (LOA). Any be translated into...

We consider the task of solving independent component analysis (ICA) problem x=As given observations x, with a constraint nonnegativity source random vector s. refer to this as nonnegative and we methods for task. For sources nonzero probability density function (pdf) p(s) down s=0 it is sufficient find orthonormal rotation y=Wz prewhitened z=Vx, which minimizes mean squared error reconstruction z from rectified version y/sup +/ y. suggest some algorithms perform this, both based on...

We propose the audio inpainting framework that recovers portions of data distorted due to impairments such as impulsive noise, clipping, and packet loss. In this framework, are treated missing their location is assumed be known. The signal decomposed into overlapping time-domain frames restoration problem then formulated an inverse per frame. Sparse representation modeling employed frame, each solved using Orthogonal Matching Pursuit algorithm together with a discrete cosine or Gabor...

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Sparse representations have proved a powerful tool in the analysis and processing of audio signals already lie at heart popular coding standards such as MP3 Dolby AAC. In this paper we give an overview number current emerging applications sparse areas from coding, enhancement music transcription to blind source separation solutions that can solve "cocktail party problem." each case will show how...

We present a simple and efficient method for beat tracking of musical audio. With the aim replicating human ability tapping in time to music, we formulate our approach using two state model. The first performs tempo induction tracks changes, while second maintains contextual continuity within single hypothesis. Beat times are recovered by passing output an onset detection function through adaptively weighted comb filterbank matrices separately identify period alignment. evaluate tracker both...

We investigate the conditions for which nonnegative matrix factorization (NMF) is unique and introduce several theorems can determine whether decomposition in fact or not. The are illustrated by examples showing use of their limitations. have shown that corruption a NMF additive noise leads to noisy estimation noise-free solution. Finally, we stochastic view analyze characterization underlying model will result an with small errors.

This paper describes a newly-launched public evaluation challenge on acoustic scene classification and detection of sound events within scene. Systems dealing with such tasks are far from exhibiting human-like performance robustness. Undermining factors numerous: the extreme variability sources interest possibly interfering, presence complex background noise as well room effects like reverberation. The proposed is an attempt to help research community move forward in defining studying...

This paper presents two new algorithms for wideband spectrum sensing at sub-Nyquist sampling rates, both single nodes and cooperative multiple nodes. In single-node sensing, a two-phase algorithm based on compressive is proposed to reduce the computational complexity improve robustness secondary users (SUs). case, signals received SUs exhibit sparsity property that yields low-rank matrix of compressed measurements fusion center. therefore leads completion. addition, are evaluated TV white...

For dictionary-based decompositions of certain types, it has been observed that there might be a link between sparsity in the dictionary and decomposition. Sparsity also associated with derivation fast efficient learning algorithms. Therefore, this paper we present greedy adaptive algorithm sets out to find sparse atoms for speech signals. The learns on data frames taken from signal. It iteratively extracts frame minimum index, adds matrix. contribution atom is then removed, process...

This article deals with learning dictionaries for sparse approximation whose atoms are both adapted to a training set of signals and mutually incoherent. To meet this objective, we employ dictionary scheme consisting followed by update add the latter decorrelation step in order reach target mutual coherence level. is accomplished an iterative projection method complemented rotation dictionary. Experiments on musical audio data comparison optimal coherence-constrained directions (MOCOD)...

Sound event detection (SED) is a task to detect sound events in an audio recording. One challenge of the SED that many datasets such as Detection and Classification Acoustic Scenes Events (DCASE) are weakly labelled. That is, there only tags for each clip without onset offset times events. We compare segment-wise clip-wise training lacking previous works. propose convolutional neural network transformer (CNN-Transfomer) tagging SED, show CNN-Transformer performs similarly recurrent (CRNN)....

Deep learning techniques have been used recently to tackle the audio source separation problem. In this work, we propose use deep fully convolutional denoising autoencoders (CDAEs) for monaural separation. We as many CDAEs number of sources be separated from mixed signal. Each CDAE is trained separate one and treats other background noise. The main idea allow each learn suitable spectral-temporal filters features its corresponding source. Our experimental results show that perform slightly...