The document discusses spatial hearing and head-related transfer functions (HRTFs) for virtual audio. It covers measuring HRTFs using a KEMAR manikin, constructing filters based on measured HRTFs to localize sound, issues with non-individualized HRTFs, synthetic HRTF approaches, and techniques for externalization like reverberation and decorrelation. Applications mentioned include immersive environments, hearing aids, and representational sounds.
Absorption Coefficients
The Sabine Equation
Reverb Calculation Example 1
Estimating the Reverberation Time
Reverb Calculation Example 2
Correcting the Reverberation Time
Control of Interfering Noise
Absorbers
Recording Solutions
b. 3 to 1 Rule
c. Working in Mono
d. Comb Filtering
Absorption Coefficients
The Sabine Equation
Reverb Calculation Example 1
Estimating the Reverberation Time
Reverb Calculation Example 2
Correcting the Reverberation Time
Control of Interfering Noise
Absorbers
Recording Solutions
b. 3 to 1 Rule
c. Working in Mono
d. Comb Filtering
1. Hearing
2. Stereophonic Sound & The Man Who Invented Stereo
3. The Haas Effect
4. Binaural Recording
5. HRTF
6. Stereo Microphone Techniques
Coincident and Non-Coincident Configuration
a. AB
b. XY
c. Mid Side
d. Blumlein
e. ORTF
7. Further Research
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Signal & Image Processing And Analysis For Scientists And Engineers Technical...Jim Jenkins
This three-day course is designed is designed for engineers, scientists, technicians, implementers, and managers who need to understand basic and advanced methods of signal and image processing and analysis techniques for the measurement and imaging sciences. This course will jump start individuals who have little or no experience in the field to implement these methods, as well as provide valuable insight, new methods, and examples for those with some experience in the field.
PHOENIX AUDIO TECHNOLOGIES - A large Audio Signal Algorithm PortfolioHTCS LLC
Phoenix Audio Technology has the attached publication available which lists their Audio Signal Algorithm Portfolio, e.g. Multi Sensor Processing, Blind Source Separation, Echo and Reference Channel Canceling, Single Sensor Processing, Multi Resolution Analysis, Single Power Compression, Direction Finding, Data Tracking, Data Fusion, and more.
Ambisonics: Getting the Best Surround AroundRichard Elen
This presentation, created for the 2007 Audio Engineering Society UK Conference, describes how to distribute superior Ambisonic surround-sound audio content in a way that maximises its accessibility by requiring only standard surround replay equipment. It's a longer version of Part 4 of my Ionian University presentations.
Parts 1 and 2 of a series of four presentations that formed the basis of my short course on spatial audio for artists at the Music Department, Ionian University, Corfu, July 2008
Spatial Sound 3: Audio Rendering and AmbisonicsRichard Elen
Part 3 of a series of four presentations that formed the basis of my short course on spatial audio for artists at the Music Department, Ionian University, Corfu, July 2008
1. Hearing
2. Stereophonic Sound & The Man Who Invented Stereo
3. The Haas Effect
4. Binaural Recording
5. HRTF
6. Stereo Microphone Techniques
Coincident and Non-Coincident Configuration
a. AB
b. XY
c. Mid Side
d. Blumlein
e. ORTF
7. Further Research
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Signal & Image Processing And Analysis For Scientists And Engineers Technical...Jim Jenkins
This three-day course is designed is designed for engineers, scientists, technicians, implementers, and managers who need to understand basic and advanced methods of signal and image processing and analysis techniques for the measurement and imaging sciences. This course will jump start individuals who have little or no experience in the field to implement these methods, as well as provide valuable insight, new methods, and examples for those with some experience in the field.
PHOENIX AUDIO TECHNOLOGIES - A large Audio Signal Algorithm PortfolioHTCS LLC
Phoenix Audio Technology has the attached publication available which lists their Audio Signal Algorithm Portfolio, e.g. Multi Sensor Processing, Blind Source Separation, Echo and Reference Channel Canceling, Single Sensor Processing, Multi Resolution Analysis, Single Power Compression, Direction Finding, Data Tracking, Data Fusion, and more.
Ambisonics: Getting the Best Surround AroundRichard Elen
This presentation, created for the 2007 Audio Engineering Society UK Conference, describes how to distribute superior Ambisonic surround-sound audio content in a way that maximises its accessibility by requiring only standard surround replay equipment. It's a longer version of Part 4 of my Ionian University presentations.
Parts 1 and 2 of a series of four presentations that formed the basis of my short course on spatial audio for artists at the Music Department, Ionian University, Corfu, July 2008
Spatial Sound 3: Audio Rendering and AmbisonicsRichard Elen
Part 3 of a series of four presentations that formed the basis of my short course on spatial audio for artists at the Music Department, Ionian University, Corfu, July 2008
OwnSurround HRTF Service for ProfessionalsTomi Huttunen
Head-related transfer functions (HRTFs) are an essential part of 3D headphone audio. OwnSurround has developed a simulation based service for the fast generation of personalized HRTFs.
Hybrid Reverberator Using Multiple Impulse Responses for Audio Rendering Impr...a3labdsp
In the recent years, hybrid reverberation algorithms have been widely explored aiming to reproduce the acoustic behavior of real environment at low computational load. On this basis, exploiting the advantages introduced from hybrid reverberation structures, a novel approach for the reproduction of moving listener position through impulse responses (IR) interpolation has been presented in this paper. In particular, the presented methodology allows to remove
redundant information in large IR database also decreasing the memory usage and the computational complexity required to perform the auralization operation. The effectiveness of the proposed approach has been proved taking into account a real IR database and also providing comparison with the existing state-of-art techniques in terms of objective and subjective measures.
Noise reduction is the process of removing noise from a signal. In this project, two audio files are given: (1) speech.au and (2) noisy_speech.au. The first file contains the original speech signal and the second one contains the noisy version of the first signal. The objective of this project is to reduce the noise from the noisy file
Echo and reverberation effects are used extensively in the music industry. Here we will design a digital filter that will create the echo and reverb effect on audio signals.
A Comparative Study: Gammachirp Wavelets and Auditory Filter Using Prosodic F...CSCJournals
Modern automatic speech recognition (ASR) systems typically use a bank of linear filters as the first step in performing frequency analysis of speech. On the other hand, the cochlea, which is responsible for frequency analysis in the human auditory system, is known to have a compressive non-linear frequency response which depends on input stimulus level. It will be shown in this paper that it presents a new method on the use of the gammachirp auditory filter based on a continuous wavelet analysis. The essential characteristic of this model is that it proposes an analysis by wavelet packet transformation on the frequency bands that come closer the critical bands of the ear that differs from the existing model based on an analysis by a short term Fourier transformation (STFT). The prosodic features such as pitch, formant frequency, jitter and shimmer are extracted from the fundamental frequency contour and added to baseline spectral features, specifically, Mel Frequency Cepstral Coefficients (MFCC) for human speech, Gammachirp Filterbank Cepstral Coefficient (GFCC) and Gammachirp Wavelet Frequency Cepstral Coefficient (GWFCC). The results show that the gammachirp wavelet gives results that are comparable to ones obtained by MFCC and GFCC. Experimental results show the best performance of this architecture. This paper implements the GW and examines its application to a specific example of speech. Implications for noise robust speech analysis are also discussed within AURORA databases.
Audio Morphing for Percussive Sound Generationa3labdsp
The aim of audio morphing algorithms is to combine two or more sounds to create a new sound with intermediate timbre and duration. During the last two decades several efforts have been made to improve morphing algorithms in order to obtain more realistic and perceptually relevant sounds. In this paper we present an automatic audio morphing technique applied to percussive musical instruments. Based on preprocessing of the sound references in frequency domain and linear interpolation in time domain, the presented approach allows one to generate high quality hybrid sounds at a low computational cost. Several results are reported in order to show the effectiveness of the proposed approach in terms of audio quality and acoustic perception of the generated hybrid sounds, taking into consideration different percussive samples. Mean opinion score and multidimensional scaling were used to compare the presented approach with existing state of the art techniques.
An efficient peak valley detection based vad algorithm for robust detection o...csandit
Biometrics is science of measuring and statistically analyzing biological data. Biometric system
establishes identity of a person based on unique physical or behavioral characteristic possessed
by an individual. Behavioral biometrics measures characteristics which are acquired naturally
over time. Physical biometrics measures inherent physical characteristics on an individual.
Over the last few decades enormous attention is drawn towards ocular biometrics. Cues
provided by ocular region have led to exploration of newer traits. Feasibility of periocular
region as a useful biometric trait has been explored recently. With the promising results of
preliminary examination, research towards periocular region is currently gaining lot of
prominence. Researchers have analyzed various techniques of feature extraction and
classification in the periocular region. This paper investigates the effect of using Lower Central
Periocular Region (LCPR) for identification. The results obtained are comparable with those
acquired for full periocular texture features with an advantage of reduced periocular area.
AN EFFICIENT PEAK VALLEY DETECTION BASED VAD ALGORITHM FOR ROBUST DETECTION O...cscpconf
Voice Activity Detection (VAD) problem considers detecting the presence of speech in a noisy signal. The speech/non-speech classification task is not as trivial as it appears, and most of the VAD algorithms fail when the level of background noise increases. In this research we are presenting a new technique for Voice Activity Detection (VAD) in EEG collected brain stem speech evoked potentials data [7, 8, 9]. This one is spectral subtraction method in which we have developed our own mathematical formula for the peak valley detection (PVD) of the frequency spectra to detect the voice activity [1]. The purpose of this research is to compare the performance of this SNR based PVD (SNRPVD) method over Zero-Crossing rate detector [5] and statistical analysis based algorithms [10]. We have put into application of these three algorithms on these particular data sets of this experiment [7, 8, 9] and VAD is verified and compared the results of these three. MATLAB routines were developed on these particular methodologies. Finally we concluded that the method of SNRPVD surely performing better than the ZCR and statistical algorithms.
An efficient peak valley detection based vad algorithm for robust detection o...csandit
Voice Activity Detection (VAD) problem considers detecting the presence of speech in a noisy
signal. The speech/non-speech classification task is not as trivial as it appears, and most of the
VAD algorithms fail when the level of background noise increases. In this research we are
presenting a new technique for Voice Activity Detection (VAD) in EEG collected brain stem
speech evoked potentials data [7, 8, 9]. This one is spectral subtraction method in which we
have developed our own mathematical formula for the peak valley detection (PVD) of the
frequency spectra to detect the voice activity [1]. The purpose of this research is to compare the
performance of this SNR based PVD (SNRPVD) method over Zero-Crossing rate detector [5]
and statistical analysis based algorithms [10]. We have put into application of these three
algorithms on these particular data sets of this experiment [7, 8, 9] and VAD is verified and
compared the results of these three. MATLAB routines were developed on these particular
methodologies. Finally we concluded that the method of SNRPVD surely performing better than
the ZCR and statistical algorithms.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
2. Natural vs Virtual Spatial Hearing
Applications
Measured HRTFs Approach
Constructing filters based on Measured HRTF
Synthetic HRTFs
Problem of In-Head Localization
Externalization
◦ Reverberation
◦ Decorrelation
3.
4. Process of widening stereo image of a sound and create an
illusion of sound in three dimensional space.
Uses attributes that complement or replace the spatial
attributes that existed originally with a given sound source.
5. Immersive environments
Hearing Aids
Non Speech Audio inputs
Representational Sounds
6. Captures the frequency dependent amplitude and time delay
differences that result from the head, torso and the complex
shape of the Pinna.
Amplitude difference of signal between the two ears is called
the Inter-aural Level Difference(ILD) and the time difference is
called the Inter-aural Time Difference(ITD).
7.
8.
9. HRTFs captured using microphones placed
inside the ear of a KEMAR manikin for
different spatial locations.
y(nT) = x(nT) * h(nT)
◦ x(nT) = monophonic audio sample
◦ h(nT) = HRTF
◦ y(nT) = localized audio sample
10. Non-individualized HRTFs cause localization errors
due to unique pinna features.
Limitations in measurement.
Limitations to simulate a moving sound source.
Interpolating algorithms have varying degree of
success.
11. Using bandpass filters and delays to localize sound in
space using measured HRTFs as reference.
Easy to change the texture and timbre of the audio
sample by manually modifying the frequency
components.
Easier to simulate moving sound sources.
The process of localizing the sound at a particular point
is intricate and time consuming.
original Measured Synthetic
(e10 a250)
12.
13. An easier approach is to model the filtering
characteristics of the head and the pinna.
24. “refers to a process where the audio source
signal is transformed into multiple output
signals with waveforms that appear different
from each other but, which sound the same
as the source.”
Effects of Decorrelation:
◦ Produces diffused sound fields
◦ Prevents image shift
◦ Prevents the Precedence Effect
◦ Reduces combing
25. The cross Correlation function determines the
correlation measure
Correlation measure
◦ (+1) Identical Signals
◦ (-1) Signals are out of phase
◦ (0) Signals are dissimilar
27. The convolution operation is equivalent to a
FIR filter and the exemplar signals as its
coefficients.
Correlation measure determined by the
correlation of the filter coefficients.
FIR filter is made all pass by keeping the
magnitude specification as unity.
28. The phase is constructed by combination of
random number sequences.
Correlation measure of the output signals will
be dependent on correlation measure of the
random number sequences.
33. Improve frontal localization
Phase issues
Improve distance perception
Alternative methods of HRTF measurement
and Externalization
34. 1. Begault, D.R.: 3D Sound for Virtual Reality and Multimedia.
Academic Press, Cambridge (1994)
2. Allen, J. & Berkley, D.: Image method for efficiently simulating
small room acoustics, J. Acoust. Soc. Am., Vol 65, No. 4, April
1979
3. Kendell, G.S.: The Decorrelation of Audio Signals and Its Impact
on Spatial Imagery. Computer Music Journal, Vol. 19, No. 4.
(Winter, 1995), pp. 71-87
4. McGovern, S.G.: A Model for Room Acoustics
5. Hartmann W.M. & Wittenberg A.: On the Externalization of
Sound Images
6. Brown P. & Duda R.: An Efficient HRTF Model for 3D Sound
35. 7. Freeland, Diniz, Biscainho: Using Interpositional Transfer Functions in
3D Sound.
8. http://www.audiologyonline.com/news/news_detail.asp?news_id=6
9. Torrez, Petraglia: HRTF Interpolation in the Wavelet Transform Domain.
10. Gardner B. & Martin K.: HRTFs Measurement of a KEMAR Dummy Head
Microphone” MIT Media Lab personal Computing – Technical Report
#280 May, 1994
11. J. Blauert. Spatial Hearing. MIT Press, Cambridge, MA, 1983.
12. Johansson, P.: Sound Externalization, Luleå University of Technology
13. Wenzel, E.M., Arruda, M., Kistler, D.J., Wightman, F.L.: Localization
using nonindividualized head-related transfer functions. J. Acoust. Soc.
Am. 94(1), 111–123 (1993)
Editor's Notes
3d audio system consists of spatial sound processors that would activate the spatial hearing mechanism’s perceptual & cognitive aspects essential to forming a particular spatial judgement.
The range of frequencies at each stage!
Shoulder reflections were not modeled as they played a less than significant part in forming elevation cues.
Used the HRIR from experimental data to approximate the delays using the equation for tau(k)
http://www.2pi.us/rir.html
Dijk/c is the effective time delay of each echo. The magnitude right now is unity.
I + j +k represent the total number of reflections the sound has made. Rijk is RC for virtual sound source.