Temporal synchronization of multiple audio signals

Kammerl, Julius; Birkbeck, Neil; Inguva, Sasi; Kelly, Damien P.; Crawford, Andrew J.; Denman, Hugh; Kokaram, Anil; Pantofaru, Caroline

doi:10.1109/icassp.2014.6854474

Cited by 15 publications

(5 citation statements)

References 16 publications

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“…Kammerl et al [12] presented two graph-based approaches able to synchronize several audio signals. Features like Spectral Flatness or Zero-crossing Rate are extracted from the audio sources.…”

Section: Related Workmentioning

confidence: 99%

FPGA-Based Embedded System Implementation of Audio Signal Alignment

Stornaiuolo

Perini

Santambrogio

et al. 2019

2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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FPGAs are considered a valuable solution for embedded system applications thanks to their performance, energy efficiency and capability to face system failures. However, the number of available applications is limited due to the learning curve needed to customize FPGA-based accelerators. As proof of this, Xilinx recently released PYNQ, a platform for Zynq SoC that relies on Python and overlays to ease the integration of functionalities of the programmable logic into applications. In this work, we build upon this framework to implement an optimized embedded design for audio alignment and we integrated it in the Python applications workflow. In particular, we provide a custom accelerator designed for PYNQ and the software interface to transparently exploit the programmable logic from the Python code runs on the embedded CPU. We then compare the executions on two different devices: the PYNQ-Z1 and the Raspberry Pi 3. Our FPGA accelerated implementation is able to reach a speedup of 12.4x with respect to the PYNQ-Z1, when only the CPU is used, and a speedup of 5.5x with respect to the Raspberry Pi 3 version.

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Section: Related Workmentioning

confidence: 99%

FPGA-Based Embedded System Implementation of Audio Signal Alignment

Stornaiuolo

Perini

Santambrogio

et al. 2019

2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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“…Given a collection of User Generated audio or video Recordings (UGRs), several approaches have been proposed about how to exploit the available visual and audio content in order to identify video clips associated to the same moment of a public event, to estimate the overlap between these clips and to synchronize them along the same temporal axis. The audio content is a key to solving this problem and several works have shown that the temporal relations between different UGRs can be revealed by exploiting the correlations in their associated audio streams [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Normalization of Partly Overlapping Audio Recordings from the Same Event Based on Relative Signal Powers

Stefanakis

Mouchtaris

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Exploiting correlations in the audio, several works in the past have demonstrated the ability to automatically match and synchronize user-generated video or audio files of the same event. Such tools solve for the unknown starting and ending time of each available recording along the event time-line and open the way for collaborative content production approaches. However, a source of difficulty for collaborative processing approaches related to audio is the fact that the different audio recordings may be available at significantly different signal levels. In this paper, we present a normalization approach to automatically define gains for all the recordings so that the variations in the signal levels among different recordings are suppressed. We show that normalization is trivial when all recordings share the same time support but the same process is non-trivial when the recordings partly overlap along time, especially if the acoustic event is characterized by high dynamic variations. We demonstrate the efficiency of the proposed approach under various conditions based on real examples of user-generated audio recordings.

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“…An emerging research challenge is to investigate different means by which this low-quality but organized content can be synergistically processed and combined, so as to improve both audio and visual aspects of the captured public event (see references in [6] for applications related to visual content). This potential is examined in this paper from the perspective of…”

Section: Introductionmentioning

confidence: 99%

A subjective evaluation on mixtures of crowdsourced audio recordings

Stefanakis

Menelaos

Mouchtaris

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Exploiting correlations in the audio, several works in the past have demonstrated the ability to automatically match and synchronize User Generated Recordings (UGRs) of the same event. Considering a small number of synchronized UGRs, we formulate in this paper simple linear audio mixing approaches to combine the available audio content. We use data from two different public events to perform a comparative listening test with the goal to assess the potential of such mixtures in improving the listening experience of the captured event, as opposed to when each UGR is consumed individually. The results of the listening tests indicate that, even with just a small number of overlapping UGRs, the outcome of the mixing process gains higher preference in comparison to original UGRs played back individually.

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Temporal synchronization of multiple audio signals

Cited by 15 publications

References 16 publications

FPGA-Based Embedded System Implementation of Audio Signal Alignment

FPGA-Based Embedded System Implementation of Audio Signal Alignment

Normalization of Partly Overlapping Audio Recordings from the Same Event Based on Relative Signal Powers

A subjective evaluation on mixtures of crowdsourced audio recordings

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