Roomprints for forensic audio applications

Moore, Alastair H.; Brookes, Mike; Naylor, Patrick A.

doi:10.1109/waspaa.2013.6701854

Cited by 10 publications

(4 citation statements)

References 8 publications

(9 reference statements)

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“…RIRs are frequently used in many practical applications such as far-field speech recognition [5,2], speech enhancement [6], sound rendering [7], audio forensics [8], etc. One challenge for these applications is that existing recorded or real-world RIR datasets are collected in specialized environments and this can be a limiting factor for speech recongition.…”

Section: Introductionmentioning

confidence: 99%

IR-GAN: Room Impulse Response Generator for Far-Field Speech Recognition

Ratnarajah¹,

Tang

Manocha

2021

Interspeech 2021

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We present a Generative Adversarial Network (GAN) based room impulse response generator for generating realistic synthetic room impulse responses. Our proposed generator can create synthetic room impulse responses by parametrically controlling the acoustic features captured in real-world room impulse responses. Our GAN-based room impulse response generator (IR-GAN) is capable of improving far-field automatic speech recognition in environments not known during training. We create far-field speech training set by augmenting our synthesized room impulse responses with clean Lib-riSpeech dataset [1]. We evaluate the quality of our room impulse responses on the real-world LibriSpeech test set created using real impulse responses from BUT ReverbDB [2] and AIR [3] datasets. Furthermore, we combine our synthetic data with synthetic impulse responses generated using acoustic simulators, and this combination can reduce the word error rate by up to 14.3% in far-field speech recognition benchmarks.

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Section: Introductionmentioning

confidence: 99%

IR-GAN: Room Impulse Response Generator for Far-Field Speech Recognition

Ratnarajah¹,

Tang

Manocha

2021

Interspeech 2021

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“…Acoustic room identification is an emerging topic with many potential applications, including forensics [1], multimedia content labelling [2], robot navigation and location-aware voice interfaces. The aim is to be able to identify the specific room in which a speech recording was made given a limited number of previously seen rooms.…”

Section: Introductionmentioning

confidence: 99%

“…The aim is to be able to identify the specific room in which a speech recording was made given a limited number of previously seen rooms. A variety of features may be helpful including estimating the geometry [3,4,5,6,7], the background noise [2] and reverberation characteristics [1,8].…”

Section: Introductionmentioning

confidence: 99%

“…Frequency-dependent reverberation time is largely independent of the source-microphone arrangement [9] and has shown promising discriminative power when calculated directly from measured acoustic impulse responses (AIRs) [1]. However, blindly estimating frequency-dependent reverberation time from reverberant speech is challenging and has, thus far, received little attention.…”

Section: Introductionmentioning

confidence: 99%

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Room Identification Using Frequency Dependence of Spectral Decay Statistics

Moore

Naylor

Brookes

2018

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

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A method for room identification is proposed based on the reverberation properties of multichannel speech recordings. The approach exploits the dependence of spectral decay statistics on the reverberation time of a room. The average negative-side variance within 1 /3octave bands is proposed as the identifying feature and shown to be effective in a classification experiment. However, negative-side variance is also dependent on the direct-to-reverberant energy ratio. The resulting sensitivity to different spatial configurations of source and microphones within a room are mitigated using a novel reverberation enhancement algorithm. A classification experiment using speech convolved with measured impulse responses and contaminated with environmental noise demonstrates the effectiveness of the proposed method, achieving 79% correct identification in the most demanding condition compared to 40% using unenhanced signals.

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Noise signature identification using mobile phones for indoor localization

King,

Pinder,

Fernandez-Lanvin

et al. 2024

Multimed Tools Appl

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Indoor localization is still nowadays a challenge with room to improve. Even though there are many different approaches that have evidenced as effective, most of them require specific hardware or infrastructure deployed along the building that can be discarded in many potential scenarios. Others that do not require such on-site infrastructure, like inertial navigation-based systems, entail certain accuracy problems due to the accumulation of errors. However, this error-accumulation can be mitigated using beacons that support the recalibration of the system. The more frequently beacons are detected, the smaller will be the accumulated error. In this work, we evaluate the use of the noise signature of the rooms of a building to pinpoint the current location of a low-cost Android device. Despite this strategy is not a complete indoor localization system (two rooms could share the same signature), it allows us to generate beacons automatically. The noise recorded by the device is preprocessed performing audio filtering, audio frame segmentation, and feature extraction. We evaluated binary (determining if the ambient sound recording belonged to a specific room) and multi-class (identifying which room an ambient noise recording belonged to by comparing it amongst the remaining 18 rooms from the original 19 rooms sampled) classification methods. Our results indicate that the two Stacking techniques and K-Nearest Neighbor (KNN) machine learning classifier are the most successful methods in binary classification with an average accuracy of 99.19%, 99,08%, and 99.04%. In multi-class classification the average accuracy for KNN is 90.77%, and 90.52% and 90.15% for both Voting techniques.

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Roomprints for forensic audio applications

Cited by 10 publications

References 8 publications

IR-GAN: Room Impulse Response Generator for Far-Field Speech Recognition

IR-GAN: Room Impulse Response Generator for Far-Field Speech Recognition

Room Identification Using Frequency Dependence of Spectral Decay Statistics

Noise signature identification using mobile phones for indoor localization

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