Hidden Markov model based drone sound recognition using MFCC technique in practical noisy environments

Shi, Lin; Ahmad, Ishtiaq; He, Yujing; Chang, KyungHi

doi:10.1109/jcn.2018.000075

Cited by 78 publications

(48 citation statements)

References 31 publications

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“…For feature extraction, a 36 MFCC scheme is applied [50]. The recognition of the drone sound is accomplished despite background noise, and we evaluate the performance of the classifier.…”

Section: Methods Of Identification Of Sound Sources Using Hmm Classifiermentioning

confidence: 99%

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Classification, positioning, and tracking of drones by HMM using acoustic circular microphone array beamforming

Guo

Ahmad

Chang

2020

J Wireless Com Network

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This paper addresses issues with monitoring systems that identify and track illegal drones. The development of drone technologies promotes the widespread commercial application of drones. However, the ability of a drone to carry explosives and other destructive materials may pose serious threats to public safety. In order to reduce these threats, we propose an acoustic-based scheme for positioning and tracking of illegal drones. Our proposed scheme has three main focal points. First, we scan the sky with switched beamforming to find sound sources and record the sounds using a microphone array; second, we perform classification with a hidden Markov model (HMM) in order to know whether the sound is a drone or something else. Finally, if the sound source is a drone, we use its recorded sound as a reference signal for tracking based on adaptive beamforming. Simulations are conducted under both ideal conditions (without background noise and interference sounds) and non-ideal conditions (with background noise and interference sounds), and we evaluate the performance when tracking illegal drones.

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“…For feature extraction, a 36 MFCC scheme is applied [50]. The recognition of the drone sound is accomplished despite background noise, and we evaluate the performance of the classifier.…”

Section: Methods Of Identification Of Sound Sources Using Hmm Classifiermentioning

confidence: 99%

“…So, 36 MFCCs, including standard MFCCs and delta MFCCs for feature extraction, are applied in this paper [50].…”

Section: Details Of Feature Extraction Of Drone Soundsmentioning

confidence: 99%

Classification, positioning, and tracking of drones by HMM using acoustic circular microphone array beamforming

Guo

Ahmad

Chang

2020

J Wireless Com Network

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“…Video based detection is performed in [32]. Hidden Markov model is utilized for drone detection in [33]. An anti-Drone system is developed in [34] to detect a single drone in the surrounding field by utilizing audio, video and the RF signals.…”

Section: Ref No Year Of Publicationmentioning

confidence: 99%

“…Multiple UVAs detection [21] 2018 Ad-hoc network architectures No [22] 2017 RF signal strength No [23] 2017 Cognitive Internet of Things No [24] 2018 Image processing No [25] 2017 Image processing No [26] 2018 Machine learning No [27] 2017 Machine Learning No [28] 2016 Radar technology No [29] 2016 RF based experimental system No [30] 2018 Radio access network No [31] 2016 Sound correlation No [32] 2017 Video signal processing No [33] 2018 Hidden Markov model No In this paper, a more practical approach of the AmDr detection is proposed. The proposed approach is a combination of the supervised and the un-supervised machine learning techniques.…”

Section: Ref No Year Amdr Detetcion Technologymentioning

confidence: 99%

Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference

Uddin

Altaf

Bilal

et al. 2020

Computer Communications

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Owing to small size, sensing capabilities and autonomous nature, the Unmanned Air Vehicles (UAVs) have enormous applications in various areas e.g., remote sensing, navigation, archaeology, journalism, environmental science, and agriculture. However, the un-monitored deployment of UAVs called the amateur drones (AmDr) can lead to serious security threats and risk to human life and infrastructure. Therefore, timely detection of the AmDr is essential for the protection and security of sensitive organizations, human life and other vital infrastructure. Am-Drs can be detected using different techniques based on sound, video, thermal, and radio frequencies. However, the performance of these techniques is limited in * sever atmospheric conditions. In this paper, we propose an efficient un-supervise machine learning approach of independent component analysis (ICA) to detect various acoustic signals i.e., sounds of bird, airplanes, thunderstorm, rain, wind and the UAVs in practical scenario. After unmixing the signals, the features like Mel Frequency Cepstral Coefficients (MFCC), the power spectral density (PSD) and the Root Mean Square Value (RMS) of the PSD are extracted by using ICA. The PSD and the RMS of PSD signals are extracted by first passing the signals from octave band filter banks. Based on the above features the signals are classified using Support Vector Machines (SVM)and K Nearest Neighbor (KNN)to detect the presence or absence of AmDr. Unique feature of the proposed technique is the detection of a single or multiple AmDrs at a time in the presence of multiple acoustic interfering signals. The proposed technique is verified through extensive simulations and it is observed that the RMS values of PSD with KNN performs better than the MFCC with KNN and SVM.

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“…In the Mel-scale, the behavior is linear frequency spacing below 1,000 Hz and with logarithmic spacing over 1 kHz. In the final section, the log for Mel-spectrum is used and transferred back to the time domain to produce the MFCCs features [22]. The reader can refer to [18,21] for further information.…”

Section: Mel Frequency Cepstral Coefficients (Mfccs)mentioning

confidence: 99%

Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

Al-Kaltakchi

Taha

Shehab

et al. 2020

IJEECS

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<p><span lang="EN-GB">In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight different speakers are selected from the GRID-Audiovisual database with two females and six males. The speakers are modeled using the coupling between the Universal Background Model and Gaussian Mixture Models (GMM-UBM) in order to get a fast scoring technique and better performance. The system shows 100% in terms of speaker identification accuracy. The results illustrated that PNCCs features have better performance compared to the MFCCs features to identify females compared to male speakers. Furthermore, feature wrapping reported better performance compared to the CMVN method. </span></p>

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Hidden Markov model based drone sound recognition using MFCC technique in practical noisy environments

Cited by 78 publications

References 31 publications

Classification, positioning, and tracking of drones by HMM using acoustic circular microphone array beamforming

Classification, positioning, and tracking of drones by HMM using acoustic circular microphone array beamforming

Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference

Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

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