Audio Copy Research Articles

HADES: Hash-Based Audio Copy Detection System for Copyright Protection in Decentralized Music Sharing

Preventive measures to stop copyright infringement are yet to be implemented on current decentralized music-sharing platforms. There is no mechanism to reject modified audio before they go online, so some decentralized music platforms become places full of pirated audio files. To address this problem, a perceptual hash-based audio detection method for copyright protection in decentralized music sharing was proposed. Chromaprint, an open-source audio fingerprint program, generates a hash value to detect copyright infringement. To assess the perceptual hash technique’s robustness, Chromaprint generates a hash value from an audio file that can be modified with several signal processing attacks. The results of the detection system show that Chromaprint is very effective at spotting copyright infringement, with an average match rate of 92.64%. Deployed using a public Ethereum blockchain test network, the execution time from hashing to uploading to the IPFS distributed storage is only 616.3 ms.

Protocol for a Scalable StoryListening Intervention for Grief-Related Loneliness During COVID-19.

Social distancing during the COVID-19 pandemic limited how family, friends, and clinicians physically interacted with people who were dying and decreased communal opportunities for processing grief. These barriers can cause or exacerbate suffering due to loneliness while grieving. In this article, we describe the protocol for a brief storytelling intervention designed to reduce loneliness among families, friends, and clinicians grieving the death of a person during the time of COVID-19. We trained four StoryListening doulas (SLDs) to hold a welcoming space and listen to stories with curiosity and openness. The intervention included a video StoryListening session and two brief questionnaires, filled out before and two weeks after the encounter, assessing loneliness and quality of life. During sessions, SLDs invited participants to share their story of loss in their own words and in as much detail as preferred. When participants felt a sense of story completion, SLDs shared validating statements and expressed gratitude to the participant for sharing. The video and audio for each participant's StoryListening encounter were recorded and the participant was offered an audio copy of their session.

A Novel Anti-Collusion Audio Fingerprinting Scheme Based on Fourier Coefficients Reversing

Most anti-collusion audio fingerprinting schemes are aiming at finding colluders from the illegal redistributed audio copies. However, the loss caused by the redistributed versions is inevitable. In this letter, a novel fingerprinting scheme is proposed to eliminate the motivation of collusion attack. The audio signal is transformed to the frequency domain by the Fourier transform, and the coefficients in frequency domain are reversed in different degrees according to the fingerprint sequence. Different from other fingerprinting schemes, the coefficients of the host media are excessively modified by the proposed method in order to reduce the quality of the colluded version significantly, but the imperceptibility is well preserved. Experiments show that the colluded audio cannot be reused because of the poor quality. In addition, the proposed method can also resist other common attacks. Various kinds of copyright risks and losses caused by the illegal redistribution are effectively avoided, which is significant for protecting the copyright of audio.

A Robust Audio Fingerprinting Using a New Hashing Method

To enhance the tracking performance of illegal audio copies, we introduce a robust audio fingerprinting method against various attacks in this paper. Most audio fingerprints consist of the information in the frequency band of audio. These fingerprinting methods may lose the uniqueness of the audio fingerprint by irregular movement such as an attack with pitch value changes. The proposed fingerprint method in a fundamental frequency band makes up for the weakness of existing methods generated from frequency domain. Using the geometrical property of the proposed method, a new hashing method is employed in the similarity calculation process to compare the audio contents. In order to prove the validity of proposed algorithm, we experiment for six environments such as tempo, pitch, speed modification, noise addition, low pass filter and high pass filter. The proposed method shows the highest level of performance in most experimental environments. Especially, with respect to the tempo, pitch, and speed manipulation experiments, the proposed method archives the precision rate of the range from 95 to 100% according to the degree of manipulation, which compares favorably with the precision rate obtained by traditional approaches, and yields a precision rate between 85 and 100% in noise addition and filtering experiments.

Fast Audio Fingerprinting System Using GPU and a Clustering-Based Technique

In this paper, we present our audio fingerprinting system that detects a transformed copy of an audio from a large collection of audios in a database. The audio fingerprints in this system encode the positions of salient regions of binary images derived from a spectrogram matrix. The similarity between two fingerprints is defined as the intersection of their elements (i.e. positions of the salient regions). The search algorithm labels each reference fingerprint in the database with the closest query frame and then counts the number of matching frames when the query is overlaid over the reference. The best match is based on this count. The salient regions fingerprints together with this nearest-neighbor search give excellent copy detection results. However, for a large database, this search is time consuming. To reduce the search time, we accelerate this similarity search by using a graphics processing unit (GPU). To speed this search even further, we use a two-step search based on a clustering technique and a lookup table that reduces the number of comparisons between the query and the reference fingerprints. We also explore the tradeoff between the speed of search and the copy detection performance. The resulting system achieves excellent results on TRECVID 2009 and 2010 datasets and outperforms several state-of-the-art audio copy detection systems in detection performance, localization accuracy and run time. For a fast detection scenario with detection speed comparable to the Ellis' Shazam-based system, our system achieved the same min NDCR as the NN-based system, and significantly better detection accuracy than Ellis' Shazam-based system.

A spectrogram-based audio fingerprinting system for content-based copy detection

This paper presents a novel audio fingerprinting method that is highly robust to a variety of audio distortions. It is based on an unconventional audio fingerprint generation scheme. The robustness is achieved by generating different versions of the spectrogram matrix of the audio signal by using a threshold based on the average of the spectral values to prune this matrix. We transform each version of this pruned spectrogram matrix into a 2-D binary image. Multiple versions of these 2-D images suppress noise to a varying degree. This varying degree of noise suppression improves likelihood of one of the images matching a reference image. To speed up matching, we convert each image into an n-dimensional vector, and perform a nearest neighbor search based on this n-dimensional vector. We give results with two different feature parameters and their combination. We test this method on TRECVID 2010 content-based copy detection evaluation dataset, and we validate the performance on TRECVID 2009 dataset also. Experimental results show the effectiveness of these features even when the audio is distorted. We compare the proposed method to two state-of-the-art audio copy detection systems, namely NN-based and Shazam systems. Our method by far outperforms Shazam system for all audio transformations (or distortions) in terms of detection performance, number of missed queries and localization accuracy. Compared to NN-based system, our approach reduces minimal Normalized Detection Cost Rate (min NDCR) by 23 % and improves localization accuracy by 24 %.

A Heuristic Attack Method to PRH-Based Audio Copy Detectors

Often copyrighted multimedia files are uploaded and shared online. To avoid the unregulated spread of such material many copy detectors have been developed in order to deny the possibility to upload, and consequently make available, copies of copyrighted contents. A widely referenced fingerprint method for content-based audio identification is the Philips Robust Hash (PRH) . This paper introduces a simple but effective attack technique capable to defeat a PRH fingerprint-based audio copy detector without significantly affecting the signal quality. It is a heuristic method that adds a suitable distortion to the original audio signal, so that the modified signal is not detected as a copy of the original one but is perceptively very similar to it. The quality of the modified signal has been evaluated in terms of a distortion measure based on a mathematical model of the human auditory system and of the Peak Signal-to-Noise Ratio (PSNR). The attack method has shown a promising success rate.

A local fingerprinting approach for audio copy detection

This study proposes an audio copy detection system that is robust to various attacks. These include the severe pitch shift and tempo change attacks which existing systems fail to detect. First, we propose a novel two dimensional representation for audio signals called the time-chroma image. This image is based on a modification of the concept of chroma in the music literature and is shown to achieve better performance in song identification. Then, we propose a novel fingerprinting algorithm that extracts local fingerprints from the time-chroma image. The proposed local fingerprinting algorithm is invariant to time/frequency scale changes in audio signals. It also outperforms existing methods like SIFT to a great extent. Finally, we introduce a song identification algorithm that uses the proposed fingerprints. The resulting copy detection system is shown to significantly outperform existing methods. Besides being able to detect whether a song (or a part of it) has been copied, the proposed system can accurately estimate the amount of pitch shift and/or tempo change that were applied to a song.

CRIM’s content-based audio copy detection system for TRECVID 2009

We report results on audio copy detection for TRECVID 2009 copy detection task. This task involves searching for transformed audio queries in over 385 hours of test audio. The queries were transformed in seven different ways, three of them involved mixing unrelated speech to the original query, making it a much more difficult task. We give results with two different audio fingerprints and show that mapping each test frame to the nearest query frame (nearest-neighbor fingerprint) results in robust audio copy detection. The most difficult task in TRECVID 2009 was to detect audio copies using predetermined thresholds computed from 2008 data. We show that the nearest-neighbor fingerprints were robust to even this task and gave actual minimal normalized detection cost rate (NDCR) of around 0.06 for all the transformations. These results are close to those obtained by using the optimal threshold for each transform. This result shows the robustness of the nearest-neighbor fingerprints. These nearest-neighbor fingerprints can be efficiently computed on a graphics processing unit, leading to a very fast search.

Traceable content protection based on chaos and neural networks

In this paper, a media content encryption/decryption algorithm is designed based on a chaos system and neural networks, which generates random sequences with chaos, and encrypts or decrypts media contents with neural networks in a parallel way. In this scheme, different decryption keys can be used to recover the media content into different copies. That is, the decryption operation gets the content containing certain random sequence that can be used as the identification. With respect to this property, the scheme is used for secure content distribution. Taking the audio content for example, it is encrypted by a key at the sender side and decrypted by different keys at the receiver side. The differences between decryption keys lead to different decrypted audio copies. If one customer distributes his copy to other unauthorized customers, the chaotic sequence contained in the copy can tell the illegal customer. The performances, including security, imperceptibility and robustness, are analyzed, and some experimental results are given to show the scheme's practicability.