Fábio Felix Dias, Moacir Antonelli Ponti, Rosane Minghim
This technical report details changes applied to a noise filter to facilitate its application and improve its results. The filter is applied to denoise natural sounds recorded in the wild and to generate an acoustic index used in soundscape analysis.
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2-Dimensional spectral gating for denoising bioacoustics recordings
针对生物声学录音中噪声干扰问题，提出基于多频率结构的二维谱门控改进Noisereduce，在鸟类和海洋哺乳动物录音中实现更优降噪且不牺牲速度。
Training Set Synthesis for Bioacoustic Denoising: A Case Study With Mice
针对生物声学去噪的训练集合成方法，开发了脊线引导损失的U-Net去噪模型，以小鼠超声发声为案例，提升了脊线跟踪与USV分类性能，可推广至其他生物声学信号。
Enhancing the sound quality of historical music recordings is a long-standing problem. This paper presents a novel denoising method based on a fully-convolutional deep neural network. A two-stage U-Net model architecture is designed to model and suppress the degradations with high fidelity. The method processes the time-frequency representation of audio, and is trained using realistic noisy data to jointly remove hiss, clicks, thumps, and other common additive disturbances from old analog discs. The proposed model outperforms previous methods in both objective and subjective metrics. The resul…
Noise-Robust Voice Conversion by Conditional Denoising Training Using Latent Variables of Recording Quality and Environment
针对未见噪声下语音转换自然度受限的问题，提出用录音质量与环境潜变量条件化VC模型的去噪训练方法，并通过主客观评估验证其转换语音质量提升。
Sound recordings are used in various ecological studies, including acoustic wildlife monitoring. Such surveys require automatic detection of target sound events. However, current detectors, especially those relying on band-limited energy, are severely impacted by wind. The rapid dynamics of this noise invalidate standard noise estimators, and no satisfactory method for dealing with it exists in bioacoustics, where simple training and generalization between conditions are important. We propose to estimate the transient noise level by fitting short-term spectrum models to a wavelet packet repres…
This work describes a speech denoising system for machine ears that aims to improve speech intelligibility and the overall listening experience in noisy environments. We recorded approximately 100 hours of audio data with reverberation and moderate environmental noise using a pair of microphone arrays placed around each of the two ears and then mixed sound recordings to simulate adverse acoustic scenes. Then, we trained a multi-channel speech denoising network (MCSDN) on the mixture of recordings. To improve the training, we employ an unsupervised method, complex angular central Gaussian mixtu…
This work proposes a method for source device identification from speech recordings that applies neural-network-based denoising, to mitigate the impact of counter-forensics attacks using noise injection. The method is evaluated by comparing the impact of denoising on three state-of-the-art features for microphone classification, determining their discriminating power with and without denoising being applied. The proposed framework achieves a significant performance increase for noisy material, and more generally, validates the usefulness of applying denoising prior to device identification for…
Multi-Representation Attention Framework for Underwater Bioacoustic Denoising and Recognition
针对水下生物声学去噪与识别，提出多步骤注意力引导的MGC框架，通过频谱图分割生成软掩码并与原始输入中层融合，显著提升了分布内与分布外数据的分类准确率与泛化鲁棒性。
This paper tackles the problem of the heavy dependence of clean speech data required by deep learning based audio-denoising methods by showing that it is possible to train deep speech denoising networks using only noisy speech samples. Conventional wisdom dictates that in order to achieve good speech denoising performance, there is a requirement for a large quantity of both noisy speech samples and perfectly clean speech samples, resulting in a need for expensive audio recording equipment and extremely controlled soundproof recording studios. These requirements pose significant challenges in d…
Music discovery services let users identify songs from short mobile recordings. These solutions are often based on Audio Fingerprinting, and rely more specifically on the extraction of spectral peaks in order to be robust to a number of distortions. Few works have been done to study the robustness of these algorithms to background noise captured in real environments. In particular, AFP systems still struggle when the signal to noise ratio is low, i.e when the background noise is strong. In this project, we tackle this problematic with Deep Learning. We test a new hybrid strategy which consists…
In traditional speech denoising tasks, clean audio signals are often used as the training target, but absolutely clean signals are collected from expensive recording equipment or in studios with the strict environments. To overcome this drawback, we propose an end-to-end self-supervised speech denoising training scheme using only noisy audio signals, named Only-Noisy Training (ONT), without extra training conditions. The proposed ONT strategy constructs training pairs only from each single noisy audio, and it contains two modules: training audio pairs generated module and speech denoising modu…
We propose a method for noise reduction, the task of producing a clean audio signal from a recording corrupted by additive noise. Many common approaches to this problem are based upon applying non-negative matrix factorization to spectrogram measurements. These methods use a noiseless recording, which is believed to be similar in structure to the signal of interest, and a pure-noise recording to learn dictionaries for the true signal and the noise. One may then construct an approximation of the true signal by projecting the corrupted recording on to the clean dictionary. In this work, we build…
SNOAR: Self-training with Noise filtering, Observation adding, and Remixing for Speech Denoising Without Clean References
本文提出SNOAR框架，通过噪声过滤、观测值添加修正教师模型估计，在WHAM!和CHiME-3数据集上，其性能优于RemixIT，接近全监督基线。
We present a framework for detecting blue whale vocalisations from acoustic submarine recordings. The proposed methodology comprises three stages: i) a preprocessing step where the audio recordings are conditioned through normalisation, filtering, and denoising; ii) a label-propagation mechanism to ensure the consistency of the annotations of the whale vocalisations, and iii) a convolutional neural network that receives audio samples. Based on 34 real-world submarine recordings (28 for training and 6 for testing) we obtained promising performance indicators including an Accuracy of 85.4\% and …
Audio bandwidth extension aims to expand the spectrum of narrow-band audio signals. Although this topic has been broadly studied during recent years, the particular problem of extending the bandwidth of historical music recordings remains an open challenge. This paper proposes BEHM-GAN, a model based on generative adversarial networks, as a practical solution to this problem. The proposed method works with the complex spectrogram representation of audio and, thanks to a dedicated regularization strategy, can effectively extend the bandwidth of out-of-distribution real historical recordings. Th…
An incremental algorithm based on multichannel non-negative matrix partial co-factorization for ambient denoising in auscultation
针对听诊生物医学声音的高噪声环境降噪问题，提出基于多通道NMPCF的增量算法，在低SNR、不同噪声类型及输入延迟场景下均表现出更稳定优异的降噪性能。
Phoneme-Based Proactive Anti-Eavesdropping with Controlled Recording Privilege
针对现有超声波防窃听方案依赖能量掩蔽、易被去除且不支持授权录音的问题，提出基于音素的信息掩蔽噪声方法，优化传输策略并实现硬件原型，可将录音识别准确率降至50%以下且保留授权录音功能。
DAS-N2N: Machine learning Distributed Acoustic Sensing (DAS) signal denoising without clean data
本文提出弱监督方法DAS-N2N，通过拼接同一光缆中两根光纤获取同一信号的两个独立含噪副本训练深度学习模型，无需干净标签即可显著抑制DAS随机噪声并提升微震事件信噪比，且比传统滤波和掩蔽式自监督方法更高效。
Music Augmentation and Denoising For Peak-Based Audio Fingerprinting
本文针对噪声环境下音频指纹识别失效问题，提出并发布逼真音乐噪声增强流程及频谱图去噪深度学习模型，实验证明该模型能提升常用峰值指纹系统在噪声条件下的识别性能。
Speech Driven Video Editing via an Audio-Conditioned Diffusion Model
本文提出一种基于音频条件扩散模型的端到端语音驱动视频编辑方法，无需中间结构表示即可同步唇部与下颌运动，并在CREMA-D数据集上验证了可行性。
Enhancing Spectrogram Realism in Singing Voice Synthesis via Explicit Bandwidth Extension Prior to Vocoder
本文提出结合扩散频谱图估计与改进的Vocos声码器，在声码前进行显式带宽扩展，生成高保真歌唱语音，使听者与机器难以区分真假。
Privacy-Enhancing Infant Cry Classification with Federated Transformers and Denoising Regularization
本文提出一种结合联邦学习与去噪正则化的婴儿哭声分类方法，实现隐私保护、噪声鲁棒和通信高效。
ADNAC: Audio Denoiser using Neural Audio Codec
本文提出ADNAC，通过在大规模定制合成数据集上训练Descript Audio Codec (DAC)并结合多目标损失函数，克服传统架构局限，实现高保真音乐去噪与生成式音频修复。
DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids
本文提出DFingerNet，通过将上下文自适应引入DeepFilterNet，利用外部背景录音调节去噪过程，在助听器场景下以最小计算开销提升语音增强性能。
N-HANS is a Python toolkit for in-the-wild audio enhancement, including speech, music, and general audio denoising, separation, and selective noise or source suppression. The functionalities are realised based on two neural network models sharing the same architecture, but trained separately. The models are comprised of stacks of residual blocks, each conditioned on additional speech or environmental noise recordings for adapting to different unseen speakers or environments in real life. In addition to a Python API, a command line interface is provided to researchers and developers, both of wh…
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