Excavation equipment recognition attracts increasing attentions in recent years due to its significance in underground pipeline network protection and civil construction management. In this paper, a novel classification algorithm based on acoustics processing is proposed for four representative excavation equipments. New acoustic statistical features, namely, the short frame energy ratio, concentration of spectrum amplitude ratio, truncated energy range, and interval of pulse are first developed to characterize acoustic signals. Then, probability density distributions of these acoustic features are analyzed and a novel classifier is presented. Experiments on real recorded acoustics of the four excavation devices are conducted to demonstrate the effectiveness of the proposed algorithm. Comparisons with two popular machine learning methods, support vector machine and extreme learning machine, combined with the popular linear prediction cepstral coefficients are provided to show the generalization capability of our method. A real surveillance system using our algorithm is developed and installed in a metro construction site for real-time recognition performance validation.

Excavation Equipment Recognition Based on Novel Acoustic Statistical Features

Speaker identification systems are becoming more important in today's world. This is especially true as devices rely on the user to speak commands. In this article, an analysis of how a text-independent voice identification system can be built is presented. Extracting the Mel-Frequency Cepstral Coefficients is evaluated and a support vector machine is trained and tested on two different data sets, one from LibriSpeech and one from in-house recorded audio files. The results show the ability for such systems to be utilized in both speaker identification and speaker verification tasks.

Voice biometrics: Deep learning-based voiceprint authentication system

Urban noise recognition play a vital role in city management and safety operation, especially in the recent smart city engineering Exiting studies on urban noise recognition are mostly based on conventional acoustic features, such as Mel-Frequency Cepstral Coefficients (MFCC) and Linear Prediction Cepstral Coefficients (LPCC), and the shallow structure based classifiers, such as support vector machine (SVM) However, the urban acoustic environment is complicated and changeable Conventional acoustic representation and recognition methods may be insufficient in characterizing urban noises, and generally suffer from a degraded performance In this paper, we study the recent deep neural network based urban noise recognition The log-Mel-spectrogram, namely, the FBank feature is first derived for acoustic representation Then, the FBank spectrum constructed with a set of FBank feature vectors from multiple acoustic signal frames is fed to a convolutional neural network (CNN) for urban noise recognition Comprehensive studies on the dimension of FBank spectrums and the parameters in CNN, including the size of learnable kernels, the dropout rate, and the activation function, etc, are presented in the paper An acoustic database collected in real environment covering 11 most common urban noises with more than 56,000 samples is constructed for model verification and performance evaluation In addition, the traditional LPCC and MFCC acoustic feature combining with two popular machine learning algorithms, extreme learning machine (ELM) and support vector machine (SVM), and the FBank image feature combining with extreme learning machine (ELM), hierarchical extreme learning machine (H-ELM) and multilayer extreme learning machine (ML-ELM), have also been presented for discussions Experimental results show that the proposed method generally outperforms conventional shallow structure based classifiers

Urban noise recognition with convolutional neural network

Recently, there has been a surge in the popularity of voice-first devices, such as Amazon Echo, Google Home, etc. While these devices make our life more convenient, they are vulnerable to new attacks, such as voice replay. We develop an end-to-end system to detect replay attacks without requiring a user to wear any wearable device. Our system, called REVOLT, has several distinct features: (i) it intelligently exploits the inherent differences between the spectral characteristics of the original and replayed voice signals, (ii) it exploits both acoustic and WiFi channels in tandem, (iii) it utilizes unique breathing rate extracted from WiFi signal while speaking to test the liveness of human voice. After extensive evaluation, our voice component yields Equal Error Rate (EER) of 0.88% and 10.32% in our dataset and ASV2017 dataset, respectively; and WiFi based breathing detection achieves Breaths Per Minute (BPM) error of 1.8 up to 3m distance. We further combine WiFi and voice based detection and show the overall system offers low false positive and false negative when evaluated against a range of attacks.

Combating Replay Attacks Against Voice Assistants

Underground pipeline network surveillance system attracts increasingly attentions recently due to severe breakages caused by external excavation equipments in the mainland of China. In this paper, we study excavation equipments classification algorithm based on acoustic signal processing and machine learning algorithms. A cross-layer microphone array with four elements is designed to collect the acoustic database of representative excavation equipments on real construction sites. The generalized sidelobe canceller algorithm is employed for background noise reduction. The improved spectrum dynamic feature extraction algorithm is then implemented for the benchmark acoustic feature database construction of excavation equipments. To perform classification and background noise identification, the single hidden layer feedforward neural network is employed as the classifier. An improved algorithm based on the popular extreme learning machine (ELM) is proposed for classifier learning. The leave-one-out cross validation strategy is adopted for the regularization parameter optimization in ELM. Comprehensive experiments are conducted to test the effectiveness of the proposed algorithm. Comparisons with state-of-art classifiers and the Mel-frequency cepstrual coefficients acoustic features are also provided to demonstrate the superiority of our approach.

An enhance excavation equipments classification algorithm based on acoustic spectrum dynamic feature

This paper motivates the use of combination of mel frequency cepstral coefficients (MFCC) and its delta derivatives (DMFCC and DDMFCC) calculated using mel spaced Gaussian filter banks for text independent speaker recognition. MFCC modeled on the human auditory system shows robustness against noise and session changes and hence has become synonymous with speaker recognition. Our main aim is to test the accuracy of our proposed feature set for different values of frame overlap and MFCC feature vector sizes to identify the system having highest accuracy. Principal component analysis (PCA) is applied before the training and testing stages for feature dimensionality reduction thereby increasing computing speed and puts low constraint on the memory required for processing. The use of probabilistic neural network (PNN) in the modeling domain provided the advantages of achieving lower operational times during the training stages. The experiments examined the percentage identification accuracy (PIA) of MFCC, combination of MFCC and DMFCC as well as combination of all three feature sets MFCC, DMFCC and DDMFCC. The proposed feature set attains an identification accuracy of 94% for frame overlap of 90% and MFCC feature size of 18 coefficients. It outperforms the identification rates of the other two feature sets. These speaker recognition experiments were tested using the Voxforge database.

A unique approach in text independent speaker recognition using MFCC feature sets and probabilistic neural network

Increasing populations and industrial activity are threatening our environment. Air pollutants are emitted at a very high rate. With the technological innovation and advancement, smart cities have come into existence where Information Technology is used for better optimization and resources planning hence promoting the sustainable growth and development. Internet of Things and Data Analytics together can lead an organization to better cost management, avoiding equipment failures and improving business operations. We propose Air Quality Measurement System which can effectively keep a track over Air quality in atmosphere. Internet is used for end to end connectivity. The parameters sensed by the system are sent to the server through a Cellular Network System. Communication between device and server is deployed over lightweight Message Queuing Telemetry Transportation (MQTT) protocol.

Air Quality Parameter Measurements System Using MQTT Protocol for IoT Communication Over GSM/GPRS Technology

Power quality can be explained as the set of limits defined for a system’s electrical parameters so that the whole electrical system can function in its intended manner & performs without significant losses. Power quality constraints like power factor correction, voltage regulation, load balancing, and harmonic elimination can be maintained and fixed using Distribution Static Compensator (DSTATCOM). In the presented work, author has presented a detailed comparative study of three different effective control strategies for DSTATCOM to target reactive rower compensation and Total Harmonic Distortion (THD). The results are demonstrated in details using simulation performed in MATLAB-SIMULINK software which shows the good capability of these different control algorithms to provide good power quality for electrical system.

Anil S. Thosar

Papers

A unique approach in text independent speaker recognition using MFCC feature sets and probabilistic neural network

Air Quality Parameter Measurements System Using MQTT Protocol for IoT Communication Over GSM/GPRS Technology

Distribution System Power Quality Improvement Using D-STATCOM