Audio Processing MATLAB Projects

Ultra-Short Baseline Acoustic Positioning System

the design, implementation, and testing of an ultra short baseline (USBL) acoustic positioning system for the Amador Valley High School (AVHS) Robotics Clubs Barracuda Mark-X AUV. The system will be used to locate an underwater transducer beacon representing the final waypoint in an obstacle course designed for the AUVSI/ONR RoboSub international collegiate competition.

The USBL acoustic subsystem obtains input from the external environment via an array of four RESON TC4013 Miniature Reference Omni-directional hydrophones. The piezoelectric sensor element contained inside the rubber nibble produces an electrical signal on the order of approximately 40mv, which propogates up the coaxial cable. The electrical signal is amplified to satisfy the requirements of the dspblok ad96k42 audio I/O platform.


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Lip Detection and Adaptive Tracking

Performance of automatic speech recognition (ASR) systems utilizing only acoustic information degrades significantly in noisy environments such as a car cabins. Incorporating audio and visual information together can improve performance in these situations. This work proposes a lip detection and tracking algorithm to serve as a visual front end to an audio-visual automatic speech recognition (AVASR) system.

Several color spaces are examined that are effective for segmenting lips from skin pixels. These color components and several features are used to characterize lips and to train cascaded lip detectors. Pre-and post-processing techniques are employed to maximize detector accuracy. The trained lip detector is incorporated into an adaptive mean-shift tracking algorithm for tracking lips in a car cabin environment. The resulting detector achieves 96.8% accuracy, and the tracker is shown to recover and adapt in scenarios where mean-shift alone fails.


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Active Noise Cancellation

I tried to build a noise-cancellation system using DSK. Two microphones and one speaker were used in the system: the first microphone is to sample unexpected noises from the outside and the second microphone is to collect all the sounds, including both the desire sounds and unexpected noises. The second microphone detects and evaluates how well the noise cancellation works.

I used Least Mean Square algorithm to implement Noise Cancellation system. The output of the adaptive filters in LMS algorithm would be phase shifted by 180 degrees and sent to a speaker to generate the anti-noise sound, which would cancel noises.


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Detection of Breathing and Infant Sleep Apnea

Sleep apnea is a condition where people pause while breathing in their sleep;this can be of great concern for infants and premature babies. Current monitoring systems either require physical attachment to a user or may be unreliable. This project is meant to develop a device that can accurately detect breathing through sound and issue appropriate warnings upon its cessation. The device produced is meant to be a standalone device and thus was developed as an embedded systems project on a Xilinx Spartan 6 FPGA.


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A Guide to Producing An A Cappella CD and Development of a Pitch Detection Program

An in-depth look at the steps required to produce a CD for an a cappella group. From what microphone and preamplifiers to use, to what steps to take during the editing, mixing, and mastering processes. Finished with a look at pitch detection algorithms and how they work, and a little bit of experimentation with my own algorithm and program.


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Monophonic Pitch Recognition

The purpose of this project is to create a system that automatically converts monophonic music into its MIDI equivalent. Automatic pitch recognition allows for numerous commercial applications, including automatic transcription and digital storage of live performances. It is also desirable to be able to take an audio signal as an input and create a MIDI equivalent score because the MIDI information can be used to replace the original audio signal sounds with any sound the user would like. For example, if a piano composition is entered into the system, the resulting MIDI out could be used to trigger guitar samples. The main deliverable for this project is a DSP evaluation board that takes a monophonic analog audio signal (ex. a recorder or someone’s voice creating one pitch at a time), analyzes the signal for its fundamental frequency, and output s MIDI data that represents the pitch and timing information contained in the audio signal all in real time.


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Monophonic Pitch Recognition using MATLAB

The purpose of this project is to create a system that automatically converts monophonic music into its MIDI equivalent. Automatic pitch recognition allows for numerous commercial applications, including automatic transcription and digital storage of live performances. It is also desirable to be able to take an audio signal as an input and create a MIDI equivalent score because the MIDI information can be used to replace the original audio signal sounds with any sound the user would like. For example, if a piano composition is entered into the system, the resulting MIDI out could be used to trigger guitar samples. The main deliverable for this project is a DSP evaluation board that takes a monophonic analog audio signal (ex. a recorder or someone’s voice creating one pitch at a time), analyzes the signal for its fundamental frequency, and output s MIDI data that represents the pitch and timing information contained in the audio signal all in real time.

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Subwoofer Frequency Response Optimization by Means of Active Control

Most subwoofer systems have difficulties producing frequencies in the low end of the hearing spectrum due to the added power requirements and instabilities. Active controls can transform the audio signal without changing physical characteristics and ultimately generating a more impressive audio system. A Linkwitz transform crossover was implemented to extend the low end frequency response of a sealed enclosure. A graphical user interface in MATLAB was written to aid in selection of components, driver and enclosure volume. The circuit board was built and integrated with a home theater system inside of a couch and tested with a Real Time Analyzer. The Linkwitz crossover was shown to extend the frequency response, transient response and improve the subwoofer system while reducing the required enclosure volume.


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Development of a Voice Conversion System using MATLAB

Voice Conversion is a technique which can be used to convert or change the speech uttered by a source speaker in such a manner that it is heard as if spoken by another target speaker. Here, an approach for static voice conversion is developed and implemented. Static speech parameters are the parameters over which speaker has least control such as vocal tract structure, natural pitch of speech etc. Here, two main parameters are considered Vocal Tract Structure and Pitch. Also two different approaches are studied and implemented in MATLAB.


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Characterization of Auditory Evoked Potentials from Transient Binaural Beats Generated by Frequency Modulating Sound Stimuli

When two pure-tone (2T) stimuli with slightly different frequencies are presented independently to each ear, an auditory illusion, called binaural beats (BB), is perceived as a faint pulsation over a single tone. The frequency of the perceived tone is equal to the mean frequency of 2T and the pulsation has a rate equal to the difference of the two. The interaction of the 2T stimuli, inside the auditory cortex, can be recorded in the form of auditory steady state responses (ASSR) using conventional electroencephalography (EEG) or magnetoencephalography (MEG). The recorded ASSR usually have small amplitudes and require additional signal processing to separate them from the surrounding cortical activity.


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Swept-Tone Evoked Otoacoustic Emissions: Stimulus Calibration and Equalization

Otoacoustic Emissions (OAE) are minute acoustic responses originating from the cochlea as a result of an external acoustic stimulus and are recorded using a sensitive microphone placed in the ear canal. OAEs are acquired by synchronous stimulation with an acoustic click or tone burst and recording of the post-stimulus responses. This method of acquiring OAEs is known as transient evoked otoacoustic emissions (TEAOE) and is commonly used in clinics as a screening method for hearing and cochlear functionality in infants. Recently, a novel method of acquiring OAEs utilizing a swept-tone, or chirp, as a stimulus was developed. This method used a deconvolution process to compress the swept tone response into an impulse or click-like response. Because the human ear does not hear all frequencies (pitches) at equal loudness the swept-tone stimulus was equalized in amplitude with respect to frequency. This equalized stimulus will be perceived by the ear as equally loud in all frequencies. In this study a new hearing level equalized stimulus was designed and the OAE responses were analyzed and compared to conventional click evoked OAEs. The equalized swept-tone stimulus evoked greater magnitude OAE responses when compared to the conventional methods. It was also able to evoke responses in subjects that had little TEOAEs which might fail conventional hearing screening.


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Acquisition of Otoacoustic Emissions Using Swept-Tone Techniques

For this dissertation, high-resolution instrumentation was developed for improving the acquisition of OAEs. It was shown that a high bit-depth device is required in order to simultaneously characterize the ear canal and the cochlear responses. This led to a reduction in the stimulus artifact that revealed early latency, high-frequency otoacoustic emissions.


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De-Noising Audio Signals Using MATLAB Wavelets Toolbox

Based on the fact that noise and distortion are the main factors that limit the capacity of data transmission in telecommunications and that they also affect the accuracy of the results in the signal measurement systems, whereas, modeling and removing noise and distortions are at the core of theoretical and practical considerations in communications and signal processing. Another important issue here is that, noise reduction and distortion removal are major problems in applications such as; cellular mobile communication, speech recognition, image processing, medical signal processing, radar, sonar, and any other application where the desired signals cannot be isolated from noise and distortion.


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Music Note Recognition

The main target of the project is to get the real time estimation of the frequency of audio signal. Real time estimation will help in maintaining the data related to changes in the frequency. So we designed two different ways of estimating it. Each one has its own applications and is accurate to different types of audio.The sampling frequency is set to 44100 so that it would be compatible with all the devices.The basic approach calculates the period from the superimposition and deviation analysis of the signal. The other method is more intelligent with respect to the processing part as it uses note detection. Note detection allows us to recognise the portions of the audio sample where we can apply Fast Fourier Transformation algorithms. So this allows us to scale down the region of analysis for efficient run time. Thus we process the data obtained from the Power Spectrum and calculate the fundamental frequency. The frequency obtained from above estimations is used to evaluate the music note names.


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