Automotive three-microphone voice activity detector and noise-canceller

This paper addresses issues in improving hands-free speech recognition performance in car environments. A three-microphone array has been used to form a beamformer with leastmean squares (LMS) to improve Signal to Noise Ratio (SNR). A three-microphone array has been paralleled to a Voice Activity Detection (VAD). The VAD uses time-delay estimation together with magnitude-squared coherence (MSC)

In Car Audio

This chapter presents implementations of advanced in Car Audio Applications. The system is composed by three main different applications regarding the In Car listening and communication experience. Starting from a high level description of the algorithms, several implementations on different levels of hardware abstraction are presented, along with empirical results on both the design process undergone and the performance results achieved

Multichannel filters for speech recognition using a particle swarm optimization

Speech recognition has been used in various real-world applications such as automotive control, electronic toys, electronic appliances etc. In many applications involved speech control functions, a commercial speech recognizer is used to identify the speech commands voiced out by the users and the recognized command is used to perform appropriate operations. However, users’ commands are often corrupted by surrounding ambient noise. It decreases the effectiveness of speech recognition in order to implement the commands accurately. This paper proposes a multichannel filter to enhance noisy speech commands, in order to improve accuracy of commercial speech recognizers which work under noisy environment. An innovative particle swarm optimization (PSO) is proposed to optimize the parameters of the multichannel filter which intends to improve accuracy of the commercial speech recognizer working under noisy environment. The effectiveness of the multichannel filter was evaluated by interacting with a commercial speech recognizer, which was worked in a warehouse

Development Considerations for Implementing a Voice-Controlled Spacecraft System

As computational power and speech recognition algorithms improve, the consumer market will see better-performing speech recognition applications. The cell phone and Internet-related service industry have further enhanced speech recognition applications using artificial intelligence and statistical data-mining techniques. These improvements to speech recognition technology (SRT) may one day help astronauts on future deep space human missions that require control of complex spacecraft systems or spacesuit applications by voice. Though SRT and more advanced speech recognition techniques show promise, use of this technology for a space application such as vehicle/habitat/spacesuit requires careful considerations. This paper provides considerations and guidance for the use of SRT in voice-controlled spacecraft systems (VCSS) applications for space missions, specifically in command-and-control (C2) applications where the commanding is user-initiated. First, current SRT limitations as known at the time of this report are given. Then, highlights of SRT used in the space program provide the reader with a history of some of the human spaceflight applications and research. Next, an overview of the speech production process and the intrinsic variations of speech are provided. Finally, general guidance and considerations are given for the development of a VCSS using a human-centered design approach for space applications that includes vocabulary selection and performance testing, as well as VCSS considerations for C2 dialogue management design, feedback, error handling, and evaluation/usability testing

A Spectral Subtraction Based Algorithm for Real-time Noise Cancellation with Application to Gunshot Acoustics

This paper introduces an improved spectral subtraction based algorithm for real-time noise cancellation, applied to gunshot acoustical signals. The derivation is based on the fact that, in practice, relatively long periods without gunshot signals occur and the background noise can be modeled as being short-time stationary and uncorrelated to the impulsive gunshot signals. Moreover, gunshot signals, in general, have a spiky autocorrelation while typical vehicle noise, or related, is periodic and exhibits a wider autocorrelation. The Spectral Suppression algorithm is applied using the pre-filtering approach, as opposed to post-filtering which requires a priori knowledge of the direction of arrival of the signals of interest, namely, the Muzzle blast and the Shockwave. The results presented in this work are based on a dataset generated by combining signals from real gunshots and real vehicle noise

Spatial, Spectral, and Perceptual Nonlinear Noise Reduction for Hands-free Microphones in a Car

Speech enhancement in an automobile is a challenging problem because interference can come from engine noise, fans, music, wind, road noise, reverberation, echo, and passengers engaging in other conversations. Hands-free microphones make the situation worse because the strength of the desired speech signal reduces with increased distance between the microphone and talker. Automobile safety is improved when the driver can use a hands-free interface to phones and other devices instead of taking his eyes off the road. The demand for high quality hands-free communication in the automobile requires the introduction of more powerful algorithms. This thesis shows that a unique combination of five algorithms can achieve superior speech enhancement for a hands-free system when compared to beamforming or spectral subtraction alone. Several different designs were analyzed and tested before converging on the configuration that achieved the best results. Beamforming, voice activity detection, spectral subtraction, perceptual nonlinear weighting, and talker isolation via pitch tracking all work together in a complementary iterative manner to create a speech enhancement system capable of significantly enhancing real world speech signals. The following conclusions are supported by the simulation results using data recorded in a car and are in strong agreement with theory. Adaptive beamforming, like the Generalized Side-lobe Canceller (GSC), can be effectively used if the filters only adapt during silent data frames because too much of the desired speech is cancelled otherwise. Spectral subtraction removes stationary noise while perceptual weighting prevents the introduction of offensive audible noise artifacts. Talker isolation via pitch tracking can perform better when used after beamforming and spectral subtraction because of the higher accuracy obtained after initial noise removal. Iterating the algorithm once increases the accuracy of the Voice Activity Detection (VAD), which improves the overall performance of the algorithm. Placing the microphone(s) on the ceiling above the head and slightly forward of the desired talker appears to be the best location in an automobile based on the experiments performed in this thesis. Objective speech quality measures show that the algorithm removes a majority of the stationary noise in a hands-free environment of an automobile with relatively minimal speech distortion

Development of the Carbon Nanotube Thermoacoustic Loudspeaker

Traditional speakers make sound by attaching a coil to a cone and moving that coil back and forth in a magnetic field (aka moving coil loudspeakers). The physics behind how to generate sound via this velocity boundary condition has largely been unchanged for over a hundred years. Interestingly, around the time moving coil loudspeakers were first investigated the idea of using heat to generate sound was also known. These thermoacoustic speakers heat and cool a thin material at acoustic frequencies to generate the pressure wave (i.e. they use a thermal boundary condition). Unfortunately, when the thermoacoustic principle was initially discovered there was no material with the right properties to heat and cool fast enough. Carbon nanotube (CNT) loudspeakers first generated sound early in the 21st century. At that time there were many questions unanswered about their place in the sound generation toolbox of an engineer. The main goal of this dissertation was to continue the development of the CNT loudspeaker with focus on practical usage for an acoustic engineer. Prior to 2014, when this effort began, most of the published development work was from material scientists with objective acoustic performance data presented that was not useful beyond the scope of that particular publication. For example, low sound pressure levels in the nearfield at low power inputs was a common metric. Therefore, this effort had three main objectives with emphasis placed on acquiring data at levels and in nomenclature that would be useful to acoustic engineers so they could bring the technology to market, if adequate. Investigation into the true power efficiency of CNT loudspeakers Investigation into alternative methods to linearize the pressure response of CNT loudspeakers Investigation into the sound quality of CNT loudspeakers Overall, it was found that CNT loudspeakers are approximately four orders of magnitude less power efficient than traditional moving coil loudspeakers. The non-linear pressure output of the CNT loudspeakers can be linearized with a variety of drive signal processing methods, but the selection of which method to use depends on a variety of factors (e.g. amplification architecture available). In general, all methods studied are on the same order of magnitude power efficiency, but the direct current offset and amplitude modulation drive signal processing methods are superior in terms of sound quality