Types Of Background Noise Research Articles

PERCEPCJA MOWY W SZUMIE U MŁODYCH DOROSŁYCH Z PRAWIDŁOWYM SŁUCHEM MÓWIĄCYCH W JĘZYKU MALAJALAM

IntroductionVarious types of noise have been used with speech material to assess speech perception in noise (SPIN) abilities. The literature suggests that speech identification varies with different types of background noise, and it has been reported that the target native language and the language of the babbling influence performance. Such efforts in an Indian context have not yet been reported. The aim of the study is to evaluate the speech perception in noise performance of Malayalam-speaking young adults with normal hearing using three different background noises.Material and methodsA repeated measure research design were adopted with a random sampling method. 30 native Malayalam speakers with normal hearing between the ages of 18 and 25 participated in the study. A standardized sentence list in Malayalam was used as the speech stimulus. Nine lists were chosen and randomly divided so that there were three lists to each background noise. Noises were speech spectrum-shaped noise, non-native language multi-talker babble (Kannada), and native language multi-talker babble (Malayalam). Each successfully repeated keyword received a ‘1’ and each incorrectly repeated word received a ‘0.’ Because each sentence had four important words, each collection of 10 sentences scored a maximum of 40. The percentage of correct answers was determined and further analyzed.ResultsScores were significantly different in all three different background noises across different SNRs. The highest scores were obtained at +5 dB SNR and the poorest scores at –5 dB SNR. Among the three different background noises, native multi-talker babble (Malayalam) yielded better scores than non-native multi-talker babble (Kannada), followed last by speech spectrum-shaped noise.ConclusionsThe findings of the current study may be attributed to the increased efficacy of speech spectrum noise due to its energetic masking characteristics and the similarity between the two languages in terms of its origin and acoustic-phonetic properties.

Intelligibility of British and American English in different listening conditions

Communication specialists frequently hear complaints from patients that unfamiliar accents are more difficult to understand, particularly in background noise. Previous work (Schilaty et al., 2021) has indeed shown that when presenting American listeners with both American and British accents, intelligibility in noise is poorer for the unfamiliar accent across age and hearing status. However, Schilaty et al. (2021) examined relatively easy sentence materials (Basic English Lexicon [BEL] sentences; Calandruccio & Smiljanic, 2012) in only one type of noise. The current study expands on previous work by comparing the intelligibility of American and British accents for a more difficult sentence set (Harvard sentences; Rothauser et al., 1969) in young adult American listeners across two noise types (speech-shaped noise and 12-talker babble). Stimuli consisted of 180 Harvard sentences produced by two male talkers: one with an American (Northwestern) dialect and one with a Southern British dialect. Young adult listeners with normal hearing transcribed the sentences in quiet, in speech-shaped noise at −3 dB SNR, and in 12-talker babble at −3 dB SNR. Listeners also completed a dialect familiarity questionnaire. The results will shed additional light on the interactions between talker and listener dialect, dialect familiarity, and background noise type.

Effects of linguistic context and noise type on speech comprehension.

Understanding speech in background noise is an effortful endeavor. When acoustic challenges arise, linguistic context may help us fill in perceptual gaps. However, more knowledge is needed regarding how different types of background noise affect our ability to construct meaning from perceptually complex speech input. Additionally, there is limited evidence regarding whether perceptual complexity (e.g., informational masking) and linguistic complexity (e.g., occurrence of contextually incongruous words) interact during processing of speech material that is longer and more complex than a single sentence. Our first research objective was to determine whether comprehension of spoken sentence pairs is impacted by the informational masking from a speech masker. Our second objective was to identify whether there is an interaction between perceptual and linguistic complexity during speech processing. We used multiple measures including comprehension accuracy, reaction time, and processing effort (as indicated by task-evoked pupil response), making comparisons across three different levels of linguistic complexity in two different noise conditions. Context conditions varied by final word, with each sentence pair ending with an expected exemplar (EE), within-category violation (WV), or between-category violation (BV). Forty young adults with typical hearing performed a speech comprehension in noise task over three visits. Each participant heard sentence pairs presented in either multi-talker babble or spectrally shaped steady-state noise (SSN), with the same noise condition across all three visits. We observed an effect of context but not noise on accuracy. Further, we observed an interaction of noise and context in peak pupil dilation data. Specifically, the context effect was modulated by noise type: context facilitated processing only in the more perceptually complex babble noise condition. These findings suggest that when perceptual complexity arises, listeners make use of the linguistic context to facilitate comprehension of speech obscured by background noise. Our results extend existing accounts of speech processing in noise by demonstrating how perceptual and linguistic complexity affect our ability to engage in higher-level processes, such as construction of meaning from speech segments that are longer than a single sentence.

Autistic traits and speech perception in social and non-social noises

Individuals with the autism spectrum disorder (ASD) experience difficulties in perceiving speech in background noises with temporal dips; they also lack social orienting. We tested two hypotheses: (1) the higher the autistic traits, the lower the performance in the speech-in-noise test, and (2) individuals with high autistic traits experience greater difficulty in perceiving speech, especially in the non-vocal noise, because of their attentional bias toward non-vocal sounds. Thirty-eight female Japanese university students participated in an experiment measuring their ability to perceive speech in the presence of noise. Participants were asked to detect Japanese words embedded in vocal and non-vocal background noises with temporal dips. We found a marginally significant effect of autistic traits on speech perception performance, suggesting a trend that favors the first hypothesis. However, caution is needed in this interpretation because the null hypothesis is not rejected. No significant interaction was found between the types of background noise and autistic traits, indicating that the second hypothesis was not supported. This might be because individuals with high autistic traits in the general population have a weaker attentional bias toward non-vocal sounds than those with ASD or to the explicit instruction given to attend to the target speech.

New research on monaural speech segregation based on quality assessment

Speech enhancement (SE) is a pivotal technology in enhancing the quality and intelligibility of speech signals. Nevertheless, when processing speech signals under conditions of high signal-to-noise ratio (SNR), conventional SE techniques may inadvertently lead to a diminution in the perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI). This article introduces the innovative incorporation of the Non-Intrusive Speech Quality Assessment (NISQA) algorithm into SE systems. Through the comparison of pre and post-enhancement speech quality scores, it discerns whether the speech signal under consideration warrants enhancement processing, thereby mitigating potential deterioration in PESQ and STOI. Furthermore, this study delves into the ramifications of five prevalent speech features, namely, Mel Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), Relative Spectral Trans-formed Perceptual Linear Prediction coefficients (RASTA-PLP), Amplitude Modulation Spectrogram (AMS), and Multi-Resolution Cochleagram (MRCG), on PESQ and STOI under varying noise conditions. Experimental outcomes underscore that MRCG consistently emerges as the optimal and most stable feature for STOI, while the feature yielding the highest PESQ score exhibits intricate correlations with the background noise type, SNR level, and noise compatibility with the speech signal. Consequently, we propose an SE methodology founded on quality assessment and feature selection, facilitating the adaptive selection of optimal features tailored to distinct background noise scenarios, thereby always maintain the highest caliber enhancement effect with regard to PESQ metrics.

Effect of rail traffic noises on the perception of the acoustic environment in office spaces

This study conducted a laboratory experiment to investigate the effect of rail traffic noise on the perception of the acoustic environment in office spaces. The study considered two types of experiment stimuli, single noise, and combined noise. The single noise stimuli consisted of silence (SL), air conditioning noise (AC), irrelevant speech (SP), and six types of traffic noise (Tr). Traffic noise included road (R), maglev (Ma), tram (T), conventional train (C), high-speed train (H), and metro (Me). The combined noise stimuli used air conditioning sound and speech as background noise and combined with six types of traffic noise to form the experimental stimuli. Thirty subjects took part in laboratory experiments in which the acoustic environment was rated on two aspects: noise disturbance and acoustic comfort. Under single noises, the results showed that the noise source had a significant negative effect on the evaluation of the acoustic environment. The influence of traffic noises varied with the traffic type, which was lower and higher than that of irrelevant speech and air conditioning noise, respectively. Under combined acoustic conditions, acoustic comfort was affected by the main effects of background noise type, traffic noise type, and traffic noise level. In addition to the main effects, noise disturbance was also influenced by the interaction between traffic noise level and background noise type, and between traffic noise level and traffic noise type. Keywords: rail traffic noise; office space; acoustic environment; acoustic comfort; noise disturbance

Study of filtered-x least mean square algorithm and its different variants for active noise control of complex real-world noises

In various simulation studies for active noise control, noises are taken as tonal, multi-tonal, random broadband, and simulated chaotic noise. In reality, noises from real machines combine all these types of noises and background noise. This paper presents a comparative analysis of filtered-x least means square, filtered-e least mean square, filtered-x recursive least mean square, filtered-u least mean square, and leaky FxLMS for active noise control. Experimentally recorded noises of Band saw, CNC, Compressor, and welding processes are utilized for the analysis. The analysis is carried out for (a) linear primary and linear secondary paths, and (b) nonlinear primary and linear secondary paths. Their performance is evaluated based on steady-state residual noise and convergence time for various noises.

Neural correlation of speech envelope tracking for background noise in normal hearing.

Everyday speech communication often occurs in environments with background noise, and the impact of noise on speech recognition can vary depending on factors such as noise type, noise intensity, and the listener's hearing ability. However, the extent to which neural mechanisms in speech understanding are influenced by different types and levels of noise remains unknown. This study aims to investigate whether individuals exhibit distinct neural responses and attention strategies depending on noise conditions. We recorded electroencephalography (EEG) data from 20 participants with normal hearing (13 males) and evaluated both neural tracking of speech envelopes and behavioral performance in speech understanding in the presence of varying types of background noise. Participants engaged in an EEG experiment consisting of two separate sessions. The first session involved listening to a 12-min story presented binaurally without any background noise. In the second session, speech understanding scores were measured using matrix sentences presented under speech-shaped noise (SSN) and Story noise background noise conditions at noise levels corresponding to sentence recognitions score (SRS). We observed differences in neural envelope correlation depending on noise type but not on its level. Interestingly, the impact of noise type on the variation in envelope tracking was more significant among participants with higher speech perception scores, while those with lower scores exhibited similarities in envelope correlation regardless of the noise condition. The findings suggest that even individuals with normal hearing could adopt different strategies to understand speech in challenging listening environments, depending on the type of noise.

Advancements in end-to-end isolated Kannada ASR system by combining robust noise elimination technique and TDNN

The demonstration of the latest enhancements in the end-to-end (E2E) isolated Kannada automatic speech recognition (ASR) system, achieved by combining a robust background noise elimination technique and a time delay neural network (TDNN), is presented in this work. An E2E Kannada ASR system consists of an interactive voice response system (IVRS), ASR models, and databases containing weather and agricultural commodity prices information. In the earlier spoken query system (SQS), the presence of babble, street noise, and other background noises led to a decrease in both offline and online speech recognition accuracies. To properly train the models, we increase the size of the database by collecting the Kannada speech data from an additional 500 farmers under real-time conditions. Moreover, the proposed noise elimination technique is employed to enhance the degraded speech data. Additionally, the efficacy of the TDNN is explored to improve the recognition accuracy of ASR models and the SQS system.The outcomes of the proposed speech enhancement algorithm demonstrate the absence of audible musical noise and other types of background noises in enhanced NOIZEUS and isolated Kannada speech databases. Leveraging Kannada language resources and the amalgamation of the proposed noise reduction technique and TDNN, a significant 1.1% enhancement in speech recognition accuracy is achieved in comparison with the previously developed deep neural network-hidden Markov model (DNN-HMM) based SQS. The enhanced isolated E2E ASR SQS system undergoes testing by 500 farmers, enabling them to access real-time agricultural commodity prices and weather forecasting information in their native Kannada language/dialects. This practical validation highlights the applicability and effectiveness of our advancements in real-world scenarios. The source code of proposed noise elimination technique, experimental results of ASR models and demo conversation of SQS are made publicly available at: https://sites.google.com/view/thimmarajayadavag/downloads

The Effect of Noise on Initiation and Maintenance of Clear Speech and Associated Mental Demand.

The objectives of this study were to explore (a) the influence of different types of background noise and their informational content on the ability of speakers to initiate and maintain clear speech (CS), a widely utilized technique for enhancing speech intelligibility, and (b) the impact of background noise and CS usage on speakers' mental demand. Five adult females were asked to read sentences using both habitual and CS under four distinct noise conditions: quiet, multitalker (MT) noise, reversed multitalker (RevMT) noise, and speech-shaped (SS) noise. Following this, speakers rated their perceived level of mental demand for each speaking condition using the modified NASA Task Load Index scale. A two-part listening experiment with 48 listeners was conducted to evaluate the speakers' effectiveness in initiating and maintaining CS. Speakers initiated CS more successfully in noise than in quiet, with better performance observed in the presence of RevMT noise as compared to SS noise. Regarding the maintenance of CS, none of the speakers were successful in a quiet environment. Furthermore, the ability to maintain CS was most adversely affected in MT noise, followed by RevMT noise and SS noise. Our findings suggest that the effect of background noise on speech production is complex and multifaceted. The noise type affected speakers' ability to initiate and maintain CS as well as the mental demand associated with the speech task. The results underscore the importance of considering the characteristics of background noise and cognitive aspects of speech production when training and evaluating speakers' performance.

Evaluation of New Polish Articulation Lists (NAL-93) in the Presence of Various Speech-Like Maskers

The main goal of the research was to obtain a set of data for ability of speech in noise recognition using Polish word test (New Articulation Lists – NAL-93) with two different masking signals. The attempt was also made to standardise the background noise for Polish speech tests by creating babble noise for NAL-93. Two types of background noise were used for Polish word test – the babble noise and the speech noise. The short method was chosen in the study as it provided similar results to constant stimuli method using less word material. The experiment using both maskers was presented to 10 listeners with normal hearing. The mean SRT values for NAL-93 were −3.4 dB SNR for speech noise and 3.0 dB SNR for babble noise. In this regard, babble noise provided more efficient results. However, the SRT parameter for speech noise was more similar to values obtained for other Polish speech tests. The measurement of speech recognition using Polish word test is possible for both types of masking signals presented in the study. The decision as to which type of noise would be better in practice of hearing aid prosthetics remains an open-end question.

Incorporating automatic speech recognition methods into the transcription of police-suspect interviews: factors affecting automatic performance

IntroductionIn England and Wales, transcripts of police-suspect interviews are often admitted as evidence in courts of law. Orthographic transcription is a time-consuming process and is usually carried out by untrained transcribers, resulting in records that contain summaries of large sections of the interview and paraphrased speech. The omission or inaccurate representation of important speech content could have serious consequences in a court of law. It is therefore clear that investigation into better solutions for police-interview transcription is required. This paper explores the possibility of incorporating automatic speech recognition (ASR) methods into the transcription process, with the goal of producing verbatim transcripts without sacrificing police time and money. We consider the potential viability of automatic transcripts as a “first” draft that would be manually corrected by police transcribers. The study additionally investigates the effects of audio quality, regional accent, and the ASR system used, as well as the types and magnitude of errors produced and their implications in the context of police-suspect interview transcripts.MethodsSpeech data was extracted from two forensically-relevant corpora, with speakers of two accents of British English: Standard Southern British English and West Yorkshire English (a non-standard regional variety). Both a high quality and degraded version of each file was transcribed using three commercially available ASR systems: Amazon, Google, and Rev.ResultsSystem performance varied depending on the ASR system and the audio quality, and while regional accent was not found to significantly predict word error rate, the distribution of errors varied substantially across the accents, with more potentially damaging errors produced for speakers of West Yorkshire English.DiscussionThe low word error rates and easily identifiable errors produced by Amazon suggest that the incorporation of ASR into the transcription of police-suspect interviews could be viable, though more work is required to investigate the effects of other contextual factors, such as multiple speakers and different types of background noise.

Deep neural network analysis models for complex random telegraph signals

Time-fluctuating signals are ubiquitous and diverse in many physical, chemical, and biological systems, among which random telegraph signals (RTSs) refer to a series of instantaneous switching events between two discrete levels from single-particle movements. A reliable RTS analysis is a crucial prerequisite to identify underlying mechanisms related to device performance and sensitivity. When numerous levels are involved, complex patterns of multilevel RTSs occur and make their quantitative analysis exponentially difficult, hereby systematic approaches are often elusive. In this work, we present a three-step analysis protocol via progressive knowledge-transfer, where the outputs of the early step are passed onto a subsequent step. Especially, to quantify complex RTSs, we resort to three deep neural network architectures whose trained models can process raw temporal data directly. We furthermore demonstrate the model accuracy extensively with a large dataset of different RTS types in terms of additional background noise types and amplitude size. Our protocol offers structured schemes to extract the parameter values of complex RTSs as imperative information with which researchers can draw meaningful and relevant interpretations and inferences of given devices and systems.

Rational speech comprehension: Interaction between predictability, acoustic signal, and noise.

During speech comprehension, multiple sources of information are available to listeners, which are combined to guide the recognition process. Models of speech comprehension posit that when the acoustic speech signal is obscured, listeners rely more on information from other sources. However, these models take into account only word frequency information and local contexts (surrounding syllables), but not sentence-level information. To date, empirical studies investigating predictability effects in noise did not carefully control the tested speech sounds, while the literature investigating the effect of background noise on the recognition of speech sounds does not manipulate sentence predictability. Additionally, studies on the effect of background noise show conflicting results regarding which noise type affects speech comprehension most. We address this in the present experiment. We investigate how listeners combine information from different sources when listening to sentences embedded in background noise. We manipulate top-down predictability, type of noise, and characteristics of the acoustic signal, thus creating conditions which differ in the extent to which a specific speech sound is masked in a way that is grounded in prior work on the confusability of speech sounds in noise. Participants complete an online word recognition experiment. The results show that participants rely more on the provided sentence context when the acoustic signal is harder to process. This is the case even when interactions of the background noise and speech sounds lead to small differences in intelligibility. Listeners probabilistically combine top-down predictions based on context with noisy bottom-up information from the acoustic signal, leading to a trade-off between the different types of information that is dependent on the combination of a specific type of background noise and speech sound.

Toward Accessible Hearing Care: The Development of a Versatile Arabic Word-in-Noise Screening Tool: A Pilot Study

Speech-in-noise tests are used to assess the ability of the human auditory system to perceive speech in a noisy environment. Early diagnosis of hearing deficits helps health professionals to plan for the most appropriate management. However, hospitals and auditory clinics have a shortage of reliable Arabic versions of speech-in-noise tests. Additionally, access to specialized healthcare facilities is associated with socioeconomic status. Hence, individuals with compromised socioeconomic status do not have proper access to healthcare. Thus, In the current study, a mobile and cost-effective Arabic speech-in-noise test was developed and tested on 30 normal-hearing subjects, and their ability to perceive words-in-noise was evaluated. Moreover, a comparison between two different background noises was explored (multi-talker babble noise and white noise). The results revealed a significant difference in the thresholds between the two types of background noises. The percent-correct scores ranged from 100% to 54.17% for the white background noise and 91.57% to 50% for the multi-talker babble background noise. The proposed Arabic word-in-noise screening tool has the potential to be used effectively to screen for deteriorated speech perception abilities, particularly in low-resource settings.

Non-intrusive speech quality assessment using context-aware neural networks

To meet the human perceived quality of experience (QoE) while communicating over various Voice over Internet protocol (VoIP) applications, for example Google Meet, Microsoft Skype, Apple FaceTime, etc. a precise speech quality assessment metric is needed. The metric should be able to detect and segregate different types of noise degradations present in the surroundings before measuring and monitoring the quality of speech in real-time. Our research is motivated by the lack of clear evidence presenting speech quality metric that can firstly distinguish different types of noise degradations before providing speech quality prediction decision. To that end, this paper presents a novel non-intrusive speech quality assessment metric using context-aware neural networks in which the noise class (context) of the degraded or noisy speech signal is first identified using a classifier then deep neutral networks (DNNs) based speech quality metrics (SQMs) are trained and optimized for each noise class to obtain the noise class-specific (context-specific) optimized speech quality predictions (MOS scores). The noisy speech signals, that is, clean speech signals degraded by different types of background noises are taken from the NOIZEUS speech corpus. Results demonstrate that even in the presence of less number of speech samples available from the NOIZEUS speech corpus, the proposed metric outperforms in different contexts compared to the metric where the contexts are not classified before speech quality prediction.

Degradation-aware and color-corrected network for underwater image enhancement

Underwater image enhancement has played an important role in marine resource monitoring and preservation. However, the visual quality of underwater images is degraded due to light attenuation, background noise and water types, which brings challenging problems to underwater image enhancement. Existing Convolutional Neural Network (CNN) based methods do not fully explore degradation issues in different underwater scenes and also ignore color corrected priors, which limit their generalization and representation ability. To address the above mentioned problems, we propose a new Degradation-aware and Color-Corrected Network (DCN). Since it is difficult to estimate the degradation information of underwater image, we use Contrastive Learning (CL) to learn accurate degradation representation from input underwater images via a new degradation encoder. Then, to make the enhancement process adapt to different underwater images with the help of learned degradation representation and a color corrected prior, the DCN is built based on the proposed Degradation-aware and Color-corrected Block (DCB) for enhancing underwater images, which contains a degradation-aware branch, a color-corrected branch and an Edge Sharpness Layer (ESL). The degradation-aware branch recovers the clear image with the help of the degradation representation, while the color-corrected branch based on the color corrected prior compensates for the loss of information in the red channel. Furthermore, the ESL based on the unsharp masking principle is proposed and inserted at the end of the DCB for preserving details and sharpening edges. Such a design enables the DCN enhance underwater images, correct colors and sharpen edges simultaneously. The experiments on both synthetic and real images demonstrate that our method achieves pleasing visual results and outperforms state-of-the-art methods by a large margin.

Noncontact machinery operation status monitoring system with gated recurrent unit model

In manufacturing industry, assembly line monitoring provides statistical information about overall performance and reliability of the legacy machines, ensuring that the machines give maximum yield output. However, most legacy machines lack internet connectivity and advanced functionality, increasing the difficulty for tracking task. Therefore, this work seeks to introduce a noncontact acoustic method to track machines rather than the mainstream vibrational approach. In order to provide accurate tracking of the daily machine operation for our machine tracking system, we consider scenario of background noises such as environmental sounds from multiple sources as well as neighbouring machine?s sound. Thus, several neural networks are employed to recognize the machine status accurately. The objective of our work is to investigate the effect of machine types and states on recognition performance of neural network models under extremely noisy environments as well as to demonstrate the possibility of recognizing the sound on edge device. The main contribution of this article is the proposal of lightweight recurrent and convolutional-based models for machine sound recognition. The experimental results of our extensive testing included with multiple types of machines and background noises show that the proposed system with gated recurrent unit model has the best recognition accuracy of F1 score 0.913 with standard uncertainty of 0.026 with decent inference speed on edge device.

Age Differences in Speech Perception in Noise and Sound Localization in Individuals With Subjective Normal Hearing.

Hearing loss in old age, which often goes untreated, has far-reaching consequences. Furthermore, reduction of cognitive abilities and dementia can also occur, which also affects quality of life. The aim of this study was to investigate the hearing performance of seniors without hearing complaints with respect to speech perception in noise and the ability to localize sounds. Results were tested for correlations with age and cognitive performance. The study included 40 subjects aged between 60 and 90 years (mean age: 69.3 years) with not self-reported hearing problems. The subjects were screened for dementia. Audiological tests included pure-tone audiometry and speech perception in two types of background noise (continuous and amplitude-modulated noise) which was either co-located or spatially separated (multi-source noise field, MSNF) from the target speech. Sound localization ability was assessed and hearing performance was self-evaluated by a questionnaire. Speech in noise and sound localization was compared with young normal hearing adults. Although considering themselves as hearing normal, 17 subjects had at least a mild hearing loss. There was a significant negative correlation between hearing loss and dementia screening (DemTect) score. Speech perception in noise decreased significantly with age. There were significant negative correlations between speech perception in noise and DemTect score for both spatial configurations. Mean SRTs obtained in the co-located noise condition with amplitude-modulated noise were on average 3.1 dB better than with continuous noise. This gap-listening effect was severely diminished compared to a younger normal hearing subject group. In continuous noise, spatial separation of speech and noise led to better SRTs compared to the co-located masker condition. SRTs in MSNF deteriorated in modulated noise compared to continuous noise by 2.6 dB. Highest impact of age was found for speech perception scores using noise stimuli with temporal modulation in binaural test conditions. Mean localization error was in the range of young adults. Mean amount of front/back confusions was 11.5% higher than for young adults. Speech perception tests in the presence of temporally modulated noise can serve as a screening method for early detection of hearing disorders in older adults. This allows for early prescription of hearing aids.

Active acoustic scene monitoring through spectro-temporal modulation filtering for intruder detection.

An indoor acoustic scene monitoring system using a periodic impulse signal was previously developed. Compared with the impulse signal, the chirp signal is more robust against environmental noise due to its specific spectro-temporal structure. Such specific structure makes the chirp sound easily detected using a spectro-temporal modulation filtering mechanism. In this paper, we demonstrated a system that employs a two-dimensional spectro-temporal filtering mechanism on a Fourier spectrogram to measure the total energy of the reverberations of the chirp signal as the representation of the acoustic scene. The system compares the reverberation energy difference between consecutive chirps with a predefined threshold to automatically detect the change in the acoustic scene. Simulations were conducted in real living rooms with various types of background noise. Test results demonstrated that the proposed system is much more effective than previously developed systems for detecting the acoustic scene changes due to the intruder silently walking in the rooms. In the biggest test room (18 × 9.8 × 2.5 m3) with heavy background noise, the proposed system can still yield a correct identification rate higher than 80% to the intruder walking at 7 m from the microphone without producing any false alarms.