We have collected the most relevant information on Robust Audio. Open the URLs, which are collected below, and you will find all the info you are interested in.
Robust audio watermarking using perceptual masking1
https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/Robust20Audio20Watermarking20Using20Perceptual20Masking.pdf
The watermark must be robust to signal distor-tions,incidentalandintentional,appliedtothehost data. For example, in most applications involving storage and transmission of audio, a lossy coding operation is performed on the audio to reduce bit-rates and increase eƒciency. Operations which damage the host audio also damage the embedded watermark.
[2202.01784] Robust Audio Anomaly Detection
https://arxiv.org/abs/2202.01784
[Submitted on 3 Feb 2022] Robust Audio Anomaly Detection Wo Jae Lee, Karim Helwani, Arvindh Krishnaswamy, Srikanth Tenneti We propose an outlier robust multivariate time series model which can be used for detecting previously unseen anomalous sounds based on noisy training data.
Robust Audio Watermarking Using Perceptual Masking ...
https://www.microsoft.com/en-us/research/publication/robust-audio-watermarking-using-perceptual-masking/
Robust Audio Watermarking Using Perceptual Masking. We present a watermarking procedure to embed copyright protection into digital audio by directly modifying the audio samples. Our audio-dependent watermarking procedure directly exploits temporal and frequency perceptual masking to guarantee that the embedded watermark is inaudible and …
ROBUST AUDIO ANOMALY DETECTION
https://assets.amazon.science/aa/21/ab2fe4244139a9faa13dbe8089ce/robust-audio-anomaly-detection.pdf
ROBUST AUDIO ANOMALY DETECTION Wo Jae Leey Purdue University Karim Helwani , Srikanth Tenneti, & Arvindh Krishnaswamy Amazon Web Services [email protected] ABSTRACT We propose an outlier robust multivariate time series model which can be used for detecting previously unseen anomalous sounds based on noisy training data.
Robust audio-visual speech recognition under noisy audio ...
https://pubmed.ncbi.nlm.nih.gov/23757540/
This paper presents the maximum weighted stream posterior (MWSP) model as a robust and efficient stream integration method for audio-visual speech recognition in environments, where the audio or video streams may be subjected to unknown and time-varying corruption.
[2110.11499] Wav2CLIP: Learning Robust Audio ...
https://arxiv.org/abs/2110.11499
Wav2CLIP: Learning Robust Audio Representations From CLIP Ho-Hsiang Wu, Prem Seetharaman, Kundan Kumar, Juan Pablo Bello We propose Wav2CLIP, a robust audio representation learning method by distilling from Contrastive Language-Image Pre-training (CLIP).
Robust Audio Adversarial Example for a Physical Attack - IJCAI
https://www.ijcai.org/Proceedings/2019/0741.pdf
generate a robust audio adversarial example that can attack speech recognition models in the physical world. To the best of our knowledge, this is the rst approach to succeed in gen-erating such adversarial examples that can attack complex speech recognition models based on recurrent networks, such
GitHub - hiromu/robust_audio_ae: Robust Audio …
https://github.com/hiromu/robust_audio_ae
Robust Audio Adversarial Example for a Physical Attack. This repository includes the implementation of our paper: Robust Audio Adversarial Example for a Physical Attack. You can find generated examples at our project page.. Usage
A Highly Robust Audio Fingerprinting System
https://ismir2002.ismir.net/proceedings/02-FP04-2.pdf
A Highly Robust Audio Fingerprinting System it is sufficient to store the set of hash values h i = H{(Y i)}, and to compare H(X) with this set of hash values. At first one might think that cryptographic hash functions are a
Now you know Robust Audio
Now that you know Robust Audio, we suggest that you familiarize yourself with information on similar questions.
