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Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signals to Source Position Coordinates

機(jī)譯：使用深度學(xué)習(xí)實(shí)現(xiàn)端到端聲學(xué)定位：從音頻信號(hào)到源位置坐標(biāo)

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This paper presents a novel approach for indoor acoustic source localization using microphone arrays, based on a Convolutional Neural Network (CNN). In the proposed solution, the CNN is designed to directly estimate the three-dimensional position of a single acoustic source using the raw audio signal as the input information and avoiding the use of hand-crafted audio features. Given the limited amount of available localization data, we propose, in this paper, a training strategy based on two steps. We first train our network using semi-synthetic data generated from close talk speech recordings. We simulate the time delays and distortion suffered in the signal that propagate from the source to the array of microphones. We then fine tune this network using a small amount of real data. Our experimental results, evaluated on a publicly available dataset recorded in a real room, show that this approach is able to produce networks that significantly improve existing localization methods based on SRP-PHAT strategies and also those presented in very recent proposals based on Convolutional Recurrent Neural Networks (CRNN). In addition, our experiments show that the performance of our CNN method does not show a relevant dependency on the speaker’s gender, nor on the size of the signal window being used.
機(jī)譯：本文提出了一種基于卷積神經(jīng)網(wǎng)絡(luò)（CNN）的使用麥克風(fēng)陣列進(jìn)行室內(nèi)聲源定位的新方法。在提出的解決方案中，CNN旨在使用原始音頻信號(hào)作為輸入信息來(lái)直接估計(jì)單個(gè)聲源的三維位置，并避免使用手工制作的音頻功能。鑒于可用的本地化數(shù)據(jù)數(shù)量有限，我們?cè)诒疚闹刑岢隽艘环N基于兩個(gè)步驟的訓(xùn)練策略。我們首先使用從近距離語(yǔ)音記錄生成的半合成數(shù)據(jù)來(lái)訓(xùn)練我們的網(wǎng)絡(luò)。我們模擬了從源傳播到麥克風(fēng)陣列的信號(hào)中的時(shí)間延遲和失真。然后，我們使用少量實(shí)際數(shù)據(jù)微調(diào)此網(wǎng)絡(luò)。我們對(duì)在真實(shí)房間中記錄的公開(kāi)可用數(shù)據(jù)集進(jìn)行的實(shí)驗(yàn)結(jié)果表明，這種方法能夠產(chǎn)生可顯著改善基于SRP-PHAT策略以及基于卷積遞歸神經(jīng)網(wǎng)絡(luò)的最新提議中提出的定位方法的網(wǎng)絡(luò)。網(wǎng)絡(luò)（CRNN）。另外，我們的實(shí)驗(yàn)表明，我們的CNN方法的性能并未顯示出與說(shuō)話者性別的相關(guān)性，也沒(méi)有顯示出所使用的信號(hào)窗口的大小。

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