Efficient and unique learning of in-car voice control for engineering education

This paper presents a software solution for the in-car voice control of the vehicles using an interactive educational application in MATLAB. The comparison of various digital filters for speech analysis gives the best possible solution for vehicles to opt for in-car voice features (car start, car stop, navigation on, etc). The comparative analysis of voice control is efficiently presented using adaptive, wiener, matched, lowpass, high pass and bandpass filters. This paper also deals with the fundamental concepts of digital signal processing in the form of students’ learning from the educational tool. The basic concepts in the form of fast Fourier transform (FFT), power spectral density (PSD), joint time-frequency analysis (JTFA), comparison of voice samples with and without noise, and signal to noise ratio (SNR) of the voice samples before and after filtration could enable students to perform well in the signal processing modules.

[1]  Danilo Comminiello,et al.  Steady-State Performance of Spline Adaptive Filters , 2016, IEEE Transactions on Signal Processing.

[2]  Jacob Benesty,et al.  Noise Reduction with Optimal Variable Span Linear Filters , 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[3]  Saeed Amirkhani,et al.  Design and implementation of an interactive virtual control laboratory using haptic interface for undergraduate engineering students , 2016, Comput. Appl. Eng. Educ..

[4]  Muhammad Rizwan Asif,et al.  Teaching Tool for a Control Systems Laboratory Using a Quadrotor as a Plant in MATLAB , 2017, IEEE Transactions on Education.

[5]  M. K. Bayrakceken,et al.  An educational setup for nonlinear control systems: Enhancing the motivation and learning in a targeted curriculum by experimental practices [Focus on Education] , 2013, IEEE Control Systems.

[6]  Jacob Benesty,et al.  On Optimal Frequency-Domain Multichannel Linear Filtering for Noise Reduction , 2010, IEEE Transactions on Audio, Speech, and Language Processing.

[7]  Farhad Shahnia,et al.  Motivating Power System Protection Course Students by Practical and Computer-Based Activities , 2016, IEEE Transactions on Education.

[8]  Manuel Berenguel,et al.  An Interactivity-Based Methodology to Support Control Education: How to Teach and Learn Using Simple Interactive Tools [Lecture Notes] , 2016, IEEE Control Systems.

[9]  Guo Na Design of voice control vehicle based on SPCE061A , 2009 .

[10]  Jesper Jensen,et al.  Low Complexity DFT-Domain Noise PSD Tracking Using High-Resolution Periodograms , 2009, EURASIP J. Adv. Signal Process..

[11]  Israel Cohen,et al.  Noise spectrum estimation in adverse environments: improved minima controlled recursive averaging , 2003, IEEE Trans. Speech Audio Process..

[12]  Marc Moonen,et al.  GSVD-based optimal filtering for single and multimicrophone speech enhancement , 2002, IEEE Trans. Signal Process..

[13]  Jacob Benesty,et al.  A perspective on multichannel noise reduction in the time domain , 2013 .

[14]  Richard C. Hendriks,et al.  Unbiased MMSE-Based Noise Power Estimation With Low Complexity and Low Tracking Delay , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[15]  W. Bastiaan Kleijn,et al.  Codebook-Based Bayesian Speech Enhancement for Nonstationary Environments , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[16]  R.A. Rashid,et al.  Security system using biometric technology: Design and implementation of Voice Recognition System (VRS) , 2008, 2008 International Conference on Computer and Communication Engineering.