DOCSLIB.ORG

Design of a Protocol for the Measurement of Physiological and Emotional Responses to Sound Stimuli

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Design of a Protocol for the Measurement of Physiological and Emotional Responses to Sound Stimuli DESIGN OF A PROTOCOL FOR THE MEASUREMENT OF PHYSIOLOGICAL AND EMOTIONAL RESPONSES TO SOUND STIMULI ANDRÉS FELIPE MACÍA ARANGO UNIVERSIDAD DE SAN BUENAVENTURA MEDELLÍN FACULTAD DE INGENIERÍAS INGENIERÍA DE SONIDO MEDELLÍN 2017 DESIGN OF A PROTOCOL FOR THE MEASUREMENT OF PHYSIOLOGICAL AND EMOTIONAL RESPONSES TO SOUND STIMULI ANDRÉS FELIPE MACÍA ARANGO A thesis submitted in partial fulfillment for the degree of Sound Engineer Adviser: Jonathan Ochoa Villegas, Sound Engineer Universidad de San Buenaventura Medellín Facultad de Ingenierías Ingeniería de Sonido Medellín 2017 TABLE OF CONTENTS ABSTRACT ................................................................................................................................................................ 7 INTRODUCTION ..................................................................................................................................................... 8 1. GOALS .................................................................................................................................................................... 9 2. STATE OF THE ART ........................................................................................................................................ 10 3. REFERENCE FRAMEWORK ......................................................................................................................... 15 3.1. Noise ............................................................................................................................................................ 15 3.1.1. Noise by Colors ............................................................................................................................... 15 3.1.2. Tonal noise (and Low Frequency Noise) .............................................................................. 16 3.1.3. Temporal Characteristics of Noise .......................................................................................... 17 3.1.4. Speech Noise .................................................................................................................................... 18 3.2. Binaural Hearing ..................................................................................................................................... 18 3.2.1. Binaural Signals and Dummy Head Recording ................................................................... 18 3.3. Psychoacoustic Concepts ..................................................................................................................... 19 3.3.1. Masking .............................................................................................................................................. 20 3.4. Emotions and its Measurement ........................................................................................................ 21 3.5. Digital Filters ............................................................................................................................................ 23 4. DESCRIPTION AND ELECTION OF SOUND STIMULI ........................................................................ 24 4.1. Frequency Characteristics .................................................................................................................. 24 4.1.1. Filtering .............................................................................................................................................. 25 4.2. Time Characteristics ............................................................................................................................. 26 4.3. Spatial Information ................................................................................................................................ 26 4.3.1. Dummy Head Recording ............................................................................................................. 26 4.3.2. Convolution ...................................................................................................................................... 27 4.4. Sound Pressure Level............................................................................................................................ 27 4.5. Stimuli Choice .......................................................................................................................................... 28 5. EXPERIMENT DESIGN ................................................................................................................................... 30 5.1. General Considerations ........................................................................................................................ 30 5.1.1. Participants ...................................................................................................................................... 30 5.1.2. Visual Stimuli ................................................................................................................................... 30 5.1.3. Responses to be Measured ......................................................................................................... 31 5.1.4. Measurement tools ........................................................................................................................ 31 5.1.5. Reproduction system .................................................................................................................... 31 5.1.6. Room Setup ...................................................................................................................................... 32 5.2. Sound Pressure Level Test .................................................................................................................. 32 5.3. Audiovisual Test ..................................................................................................................................... 33 6. GENERAL RESULTS AND ANALYSIS........................................................................................................ 36 6.1. Sound Pressure Level Test .................................................................................................................. 36 6.2. Audiovisual Test ..................................................................................................................................... 38 7. CONCLUSIONS .................................................................................................................................................. 43 LIST OF FIGURES Fig. 1. Noise by Colors. Spectrum of each type on noise represented with different colors. 16 Fig. 2. Low – Middle – High frequencies over White noise representation .................................. 17 Fig. 3. Binaural Hearing ..................................................................................................................................... 19 Fig. 4 Equal loudness-level or phon curves (based on values in ISO 226-2003) ....................... 20 Fig. 5 Masking patterns produced various tone maskers (masker frequency indicated in each frame). Number on curves indicate masker level. .............................................................. 21 Fig. 6. SAM (Self Assessment Manikin) ....................................................................................................... 22 Fig. 7. Digital Filter in MATLAB ...................................................................................................................... 25 Fig. 8. Dummy Head Recording Procedure ................................................................................................ 26 Fig. 9. PSD at 500Hz centered noise. ............................................................................................................ 29 Fig. 10. Sound Pressure Level Test Stimuli ................................................................................................ 33 Fig. 11. Setup for the Audiovisual Test ........................................................................................................ 34 Fig. 12. Audiovisual Stimuli Combinations ................................................................................................ 35 Fig. 16. Valence mean values for the three Frequency noises ........................................................... 37 Fig. 17. Arousal mean values for the three Frequency noises ............................................................ 37 Fig. 18. Dominance mean values for the three Frequency noises .................................................... 38 Fig. 19. Software readings when participants move their head down ........................................... 40 Fig. 20. Reactions to audiovisual stimuli .................................................................................................... 41 Fig. 21. 125 Hz filter designed in Matlab with its cut frequencies and characteristics ............ 50 Fig. 22. 500 Hz filter designed in Matlab with its cut frequencies and characteristics ............ 51 Fig. 23. 3150 Hz filter designed in Matlab with its cut frequencies and characteristics ......... 51 Fig. 24. Diagram of System Concection and Stimuli Presentation .................................................... 63 LIST OF TABLES Table I. Studies using sound stimuli ............................................................................................................. 12 Table II. Valence Results ..................................................................................................................................
Load more

Recommended publications
  • Separation of Vocal and Non-Vocal Components from Audio Clip Using Correlated Repeated Mask (CRM)
    University of New Orleans ScholarWorks@UNO University of New Orleans Theses and Dissertations Dissertations and Theses Summer 8-9-2017 Separation of Vocal and Non-Vocal Components from Audio Clip Using Correlated Repeated Mask (CRM) Mohan Kumar Kanuri [email protected] Follow this and additional works at: https://scholarworks.uno.edu/td Part of the Signal Processing Commons Recommended Citation Kanuri, Mohan Kumar, "Separation of Vocal and Non-Vocal Components from Audio Clip Using Correlated Repeated Mask (CRM)" (2017). University of New Orleans Theses and Dissertations. 2381. https://scholarworks.uno.edu/td/2381 This Thesis is protected by copyright and/or related rights. It has been brought to you by ScholarWorks@UNO with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights- holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/or on the work itself. This Thesis has been accepted for inclusion in University of New Orleans Theses and Dissertations by an authorized administrator of ScholarWorks@UNO. For more information, please contact [email protected]. Separation of Vocal and Non-Vocal Components from Audio Clip Using Correlated Repeated Mask (CRM) A Thesis Submitted to the Graduate Faculty of the University of New Orleans in partial fulfillment of the requirements for the degree of Master of Science in Engineering – Electrical By Mohan Kumar Kanuri B.Tech., Jawaharlal Nehru Technological University, 2014 August 2017 This thesis is dedicated to my parents, Mr.
    [Show full text]
  • Psychological Measurement for Sound Description and Evaluation 11
    11 Psychological measurement for sound description and evaluation Patrick Susini,1 Guillaume Lemaitre,1 and Stephen McAdams2 1Institut de Recherche et de Coordination Acoustique/Musique Paris, France 2CIRMMT, Schulich School of Music, McGill University Montréal, Québec, Canada 11.1 Introduction Several domains of application require one to measure quantities that are representa- tive of what a human listener perceives. Sound quality evaluation, for instance, stud- ies how users perceive the quality of the sounds of industrial objects (cars, electrical appliances, electronic devices, etc.), and establishes specifications for the design of these sounds. It refers to the fact that the sounds produced by an object or product are not only evaluated in terms of annoyance or pleasantness, but are also important in people’s interactions with the object. Practitioners of sound quality evaluation therefore need methods to assess experimentally, or automatic tools to predict, what users perceive and how they evaluate the sounds. There are other applications requir- ing such measurement: evaluation of the quality of audio algorithms, management (organization, retrieval) of sound databases, and so on. For example, sound-database retrieval systems often require measurements of relevant perceptual qualities; the searching process is performed automatically using similarity metrics based on rel- evant descriptors stored as metadata with the sounds in the database. The “perceptual” qualities of the sounds are called the auditory attributes, which are defined as percepts that can be ordered on a magnitude scale. Historically, the notion of auditory attribute is grounded in the framework of psychoacoustics. Psychoacoustical research aims to establish quantitative relationships between the physical properties of a sound (i.e., the properties measured by the methods and instruments of the natural sciences) and the perceived properties of the sounds, the auditory attributes.
    [Show full text]
  • Comparison of Frequency-Warped Filter Banks in Relation to Robust Features for Speaker Identification
    Recent Advances in Electrical Engineering Comparison of Frequency-Warped Filter Banks in relation to Robust Features for Speaker Identification SHARADA V CHOUGULE MAHESH S CHAVAN Electronics and Telecomm. Engg.Dept. Electronics Engg. Department, Finolex Academy of Management and KIT’s College of Engineering, Technology, Ratnagiri, Maharashtra, India Kolhapur,Maharashtra,India [email protected] [email protected] Abstract: - Use of psycho-acoustically motivated warping such as mel-scale warping in common in speaker recognition task, which was first applied for speech recognition. The mel-warped cepstral coefficients (MFCCs) have been used in state-of-art speaker recognition system as a standard acoustic feature set. Alternate frequency warping techniques such as Bark and ERB rate scale can have comparable performance to mel-scale warping. In this paper the performance acoustic features generated using filter banks with Bark and ERB rate warping is investigated in relation to robust features for speaker identification. For this purpose, a sensor mismatched database is used for closed set text-dependent and text-independent cases. As MFCCs are much sensitive to mismatched conditions (any type of mismatch of data used for training evaluation purpose) , in order to reduce the additive noise, spectral subtraction is performed on mismatched speech data. Also normalization of feature vectors is carried out over each frame, to compensate for channel mismatch. Experimental analysis shows that, percentage identification rate for text-dependent case using mel, bark and ERB warped filter banks is comparably same in mismatched conditions. However, in case of text-independent speaker identification, ERB rate warped filter bank features shows improved performance than mel and bark warped features for the same sensor mismatched condition.
    [Show full text]
  • Fundamentals of Psychoacoustics Psychophysical Experimentation
    Chapter 6: Fundamentals of Psychoacoustics • Psychoacoustics = auditory psychophysics • Sound events vs. auditory events – Sound stimuli types, psychophysical experiments – Psychophysical functions • Basic phenomena and concepts – Masking effect • Spectral masking, temporal masking – Pitch perception and pitch scales • Different pitch phenomena and scales – Loudness formation • Static and dynamic loudness – Timbre • as a multidimensional perceptual attribute – Subjective duration of sound 1 M. Karjalainen Psychophysical experimentation • Sound events (si) = pysical (objective) events • Auditory events (hi) = subject’s internal events – Need to be studied indirectly from reactions (bi) • Psychophysical function h=f(s) • Reaction function b=f(h) 2 M. Karjalainen 1 Sound events: Stimulus signals • Elementary sounds – Sinusoidal tones – Amplitude- and frequency-modulated tones – Sinusoidal bursts – Sine-wave sweeps, chirps, and warble tones – Single impulses and pulses, pulse trains – Noise (white, pink, uniform masking noise) – Modulated noise, noise bursts – Tone combinations (consisting of partials) • Complex sounds – Combination tones, noise, and pulses – Speech sounds (natural, synthetic) – Musical sounds (natural, synthetic) – Reverberant sounds – Environmental sounds (nature, man-made noise) 3 M. Karjalainen Sound generation and experiment environment • Reproduction techniques – Natural acoustic sounds (repeatability problems) – Loudspeaker reproduction – Headphone reproduction • Reproduction environment – Not critical in headphone
    [Show full text]
  • Abstract 1. Introduction the Process of Normalising Vowel Formant Data To
    COMPARING VOWEL FORMANT NORMALISATION PROCEDURES NICHOLAS FLYNN Abstract This article compares 20 methods of vowel formant normalisation. Procedures were evaluated depending on their effectiveness at neutralising the variation in formant data due to inter-speaker physiological and anatomical differences. This was measured through the assessment of the ability of methods to equalise and align the vowel space areas of different speakers. The equalisation of vowel spaces was quantified through consideration of the SCV of vowel space areas calculated under each method of normalisation, while the alignment of vowel spaces was judged through considering the intersection and overlap of scale-drawn vowel space areas. An extensive dataset was used, consisting of large numbers of tokens from a wide range of vowels from 20 speakers, both male and female, of two different age groups. Normalisation methods were assessed. The results showed that vowel-extrinsic, formant-intrinsic, speaker-intrinsic methods performed the best at equalising and aligning speakers‟ vowel spaces, while vowel-intrinsic scaling transformations were judged to perform poorly overall at these two tasks. 1. Introduction The process of normalising vowel formant data to permit accurate cross-speaker comparisons of vowel space layout, change and variation, is an issue that has grown in importance in the field of sociolinguistics in recent years. A plethora of different methods and formulae for this purpose have now been proposed. Thomas & Kendall (2007) provide an online normalisation tool, “NORM”, a useful resource for normalising formant data, and one which has opened the viability of normalising to a greater number of researchers. However, there is still a lack of agreement over which available algorithm is the best to use.
    [Show full text]
  • Perceptual Atomic Noise
    PERCEPTUAL ATOMIC NOISE Kristoffer Jensen University of Aalborg Esbjerg Niels Bohrsvej 6, DK-6700 Esbjerg [email protected] ABSTRACT signal point-of-view that creates sound with a uniform distribution and spectrum, or the physical point-of-view A noise synthesis method with no external connotation that creates a noise similar to existing sounds. Instead, is proposed. By creating atoms with random width, an attempt is made at rendering the atoms perceptually onset-time and frequency, most external connotations white, by distributing them according to the perceptual are avoided. The further addition of a frequency frequency axis using a probability function obtained distribution corresponding to the perceptual Bark from the bark scale, and with a magnitude frequency, and a spectrum corresponding to the equal- corresponding to the equal-loudness contour, by loudness contour for a given phon level further removes filtering the atoms using a warped filter corresponding the synthesis from the common signal point-of-view. to the equal-loudness contour for a given phon level. The perceptual frequency distribution is obtained by This gives a perceptual white resulting spectrum with a creating a probability density function from the Bark perceptually uniform frequency distribution. scale, and the equal-loudness contour (ELC) spectrum is created by filtering the atoms with a filter obtained in 2. NOISE the warped frequency domain by fitting the filter to a simple ELC model. An additional voiced quality Noise has been an important component since the parameter allows to vary the harmonicity. The resulting beginning of music, and recently noise music has sound is susceptible to be used in everything from loud evolved as an independent music style, sometimes noise music, contemporary compositions, to meditation avoiding the toned components altogether.
    [Show full text]
  • A. Acoustic Theory and Modeling of the Vocal Tract
    A. Acoustic Theory and Modeling of the Vocal Tract by H.W. Strube, Drittes Physikalisches Institut, Universität Göttingen A.l Introduction This appendix is intended for those readers who want to inform themselves about the mathematical treatment of the vocal-tract acoustics and about its modeling in the time and frequency domains. Apart from providing a funda­ mental understanding, this is required for all applications and investigations concerned with the relationship between geometric and acoustic properties of the vocal tract, such as articulatory synthesis, determination of the tract shape from acoustic quantities, inverse filtering, etc. Historically, the formants of speech were conjectured to be resonances of cavities in the vocal tract. In the case of a narrow constriction at or near the lips, such as for the vowel [uJ, the volume of the tract can be considered a Helmholtz resonator (the glottis is assumed almost closed). However, this can only explain the first formant. Also, the constriction - if any - is usu­ ally situated farther back. Then the tract may be roughly approximated as a cascade of two resonators, accounting for two formants. But all these approx­ imations by discrete cavities proved unrealistic. Thus researchers have have now adopted a more reasonable description of the vocal tract as a nonuni­ form acoustical transmission line. This can explain an infinite number of res­ onances, of which, however, only the first 2 to 4 are of phonetic importance. Depending on the kind of sound, the tube system has different topology: • for vowel-like sounds, pharynx and mouth form one tube; • for nasalized vowels, the tube is branched, with transmission from pharynx through mouth and nose; • for nasal consonants, transmission is through pharynx and nose, with the closed mouth tract as a "shunt" line.
    [Show full text]
  • A Parametric Sound Object Model for Sound Texture Synthesis
    Daniel Mohlmann¨ A Parametric Sound Object Model for Sound Texture Synthesis Dissertation zur Erlangung des Grades eines Doktors der Ingenieurwissenschaften | Dr.-Ing. | Vorgelegt im Fachbereich 3 (Mathematik und Informatik) der Universitat¨ Bremen im Juni 2011 Gutachter: Prof. Dr. Otthein Herzog Universit¨atBremen Prof. Dr. J¨ornLoviscach Fachhochschule Bielefeld Abstract This thesis deals with the analysis and synthesis of sound textures based on parametric sound objects. An overview is provided about the acoustic and perceptual principles of textural acoustic scenes, and technical challenges for analysis and synthesis are con- sidered. Four essential processing steps for sound texture analysis are identified, and existing sound texture systems are reviewed, using the four-step model as a guideline. A theoretical framework for analysis and synthesis is proposed. A parametric sound object synthesis (PSOS) model is introduced, which is able to describe individual recorded sounds through a fixed set of parameters. The model, which applies to harmonic and noisy sounds, is an extension of spectral modeling and uses spline curves to approximate spectral envelopes, as well as the evolution of pa- rameters over time. In contrast to standard spectral modeling techniques, this repre- sentation uses the concept of objects instead of concatenated frames, and it provides a direct mapping between sounds of different length. Methods for automatic and manual conversion are shown. An evaluation is presented in which the ability of the model to encode a wide range of different sounds has been examined. Although there are aspects of sounds that the model cannot accurately capture, such as polyphony and certain types of fast modula- tion, the results indicate that high quality synthesis can be achieved for many different acoustic phenomena, including instruments and animal vocalizations.
    [Show full text]
  • On Musiquantics
    © bei den Autoren Herausgeber: Bereich Musikinformatik, Musikwissenschaftliches Institut Johannes Gutenberg-Universität Mainz Postfach 3980 55099 Mainz ISSN 0941-0309 Clarence Barlow On Musiquantics Part I: Texts Translators 2002 from the original German Von der Musiquantenlehre: Deborah Richards, Jay Schwartz with the kind support of the Royal Conservatoire The Hague Thereafter translator and editor Clarence Barlow 2 Preface to Part I This work grew out of materials that I assembled for my class in computer music at Cologne Music University from the inception of the class in 1984 until its unfortunate termination in 2005. I named the course Zur Musiquantik (an artificial term, implying ‘on music and quantity’), whence the English title of this book stems. This material was also in my syllabus at the Royal Conservatoire in The Hague under the same English name from 1990-2006, as it has been since then in my position at the University of California Santa Barbara. Driven by the fascination of the connections between music and mathematics, acoustics, phonetics and computer science, and further by my own preoccupation with the quantification of harmony and metre, over the years I gradually put together 32 chapters with numerous illustrations. On Musiquantics has been consciously written in a very compact form, each chapter on two pages, more as a concentrated and comprehensive teaching accompaniment than as an autonomous textbook. It also fulfils a role as a reference book, especially if its content has already been assimilated. It is my hope that my former students, who applied so much patience to this material, some of them repeatedly and frequently attending the course, find in it everything they have learned from me, and that for those who have themselves gone into teaching, the book proves to be useful for their own educational work.
    [Show full text]
  • Psychoacoustics: a Brief Historical Overview
    Psychoacoustics: A Brief Historical Overview William A. Yost From Pythagoras to Helmholtz to Fletcher to Green and Swets, a centu- ries-long historical overview of psychoacoustics. Postal: Speech and Hearing Science 1 Arizona State University What is psychoacoustics? Psychoacous- PO Box 870102 tics is the psychophysical study of acous- Tempe, Arizona 85287 tics. OK, psychophysics is the study of USA the relationship between sensory per- ception (psychology) and physical vari- Email: ables (physics), e.g., how does perceived [email protected] loudness (perception) relate to sound pressure level (physical variable)? “Psy- chophysics” was coined by Gustav Fech- ner (Figure 1) in his book Elemente der Psychophysik (1860; Elements of Psycho- physics).2 Fechner’s treatise covered at least three topics: psychophysical meth- ods, psychophysical relationships, and panpsychism (the notion that the mind is a universal aspect of all things and, as Figure 1. Gustav Fechner (1801-1887), “Father of Psychophysics.” such, panpsychism rejects the Cartesian dualism of mind and body). Today psychoacoustics is usually broadly described as auditory perception or just hearing, although the latter term also includes the biological aspects of hearing (physiological acoustics). Psychoacoustics includes research involving humans and nonhuman animals, but this review just covers human psychoacoustics. With- in the Acoustical Society of America (ASA), the largest Technical Committee is Physiological and Psychological Acoustics (P&P), and Psychological Acoustics is psychoacoustics. 1 Trying to summarize the history of psychoacoustics in about 5,000 words is a challenge. I had to make difficult decisions about what to cover and omit. My history stops around 1990. I do not cover in any depth speech, music, animal bioacoustics, physiological acoustics, and architectural acoustic topics because these may be future Acoustics Today historical overviews.
    [Show full text]
  • Psychoacoustics & Hearing
    Abstract: In this lecture, the basic concepts of sound and acoustics are introduced as they relate to human hearing. Basic perceptual hearing effects such as loudness, masking, and binaural localization are described in simple terms. The anatomy of the ear and the hearing function are explained. The causes and consequences of hearing loss are shown along with strategies for compensation and suggestions for prevention. Examples are given throughout illustrating the relation between the signal processing performed by the ear and relevant consumer audio technology, as well as its perceptual consequences. Christopher J. Struck CJS Labs San Francisco, CA 19 November 2014 Frequency is perceived as pitch or tone. An octave is a doubling of frequency. A decade is a ten times increase in frequency. Like amplitude, frequency perception is also relative. Therefore, a logarithmic frequency scale is commonly used in acoustics. The normal range for healthy young humans is 20 –20 kHz. The frequency range of fundamental tones on the piano is 27 to 4186 Hz, as shown. The ranges of other instruments and voices are shown. The Threshold of Hearing at the bottom of the graph is the lowest level pure tone that can be heard at any given frequency. The Threshold of Discomfort, at the top of the graph represents the loudest tolerable level at any frequency. The shaded centre area is the range of level and frequency for speech. The larger central area is the range for music. Level Range is also commonly referred to as Dynamic Range. The subjective or perceived loudness of sound is determined by several complex factors, primarily Masking and Hearing Acuity.
    [Show full text]
  • Chapter 2 Principles of Psychoacoustics
    Chapter 2 Principles of Psychoacoustics Psychoacoustics is the science of sound perception, i.e., investigating the statistical relationships between acoustic stimuli and hearing sensations [51]. This study aims to build up the psychoacoustic model, a kind of quantitative model, which could closely match the hearing mechanism. A good understanding of the sensory response of the human auditory system (HAS) is essential to the development of psychoacoustic models for audio watermarking, where the perceptual quality of processed audio must be preserved to the greatest extent. In this chapter, the basic structure and function of the auditory system, mainly the peripheral part, are illustrated for the comprehension of human hearing. Then, the hearing threshold and auditory masking phenomenon are analyzed to pave the way for deriving the psychoacoustic models. Finally, Psychoacoustic Model 1 in ISO/MPEG standard is implemented to be utilized in our audio watermarking scheme later on. 2.1 Physiology of the Auditory System Hearing is the sense by which sound is perceived [52]. Human hearing is performed primarily by the auditory system, in which the peripheral part is of more relevance to our study. The peripheral auditory system (the ear, that portion of the auditory system not in the brain [53]) includes three components: the outer ear, the middle ear, and the inner ear, as illustrated in Fig. 2.1. The whole process of capturing the sound through the ear to create neurological signals is an intricate and ingenious procedure. First, the sound wave travels through the auditory canal and causes the eardrum to vibrate. This vibration is transmitted via the ossicles of the middle ear to the oval window at the cochlea inlet.
    [Show full text]