ヒトの高周波振動知覚の類似特性に基づく触覚変調

Tohoku University昆陽雅司課

Perceptual Model-Driven Authoring of Plausible Vibrations from User Expectations for Virtual Environments

One of the central goals of design is the creation of experiences that are rated favorably in the intended application context. User expectations play an integral role in tactile product quality and tactile plausibility judgments alike. In the vibrotactile authoring process for virtual environments, vibra-tion is created to match the user’s expectations of the presented situational context. Currently, inefficient trial and error approaches attempt to match expectations implicitly. A more efficient, model-driven procedure based explicitly on tactile user expectations would thus be beneficial for author-ing vibrations. In everyday life, we are frequently exposed to various whole-body vibrations. Depending on their temporal and spectral proper-ties we intuitively associate specific perceptual properties such as “tin-gling”. This suggests a systematic relationship between physical parame-ters and perceptual properties. To communicate with potential users about such elicited or expected tactile properties, a standardized design language is proposed. It contains a set of sensory tactile perceptual attributes, which are sufficient to characterize the perceptual space of vibration encountered in everyday life. This design language enables the assessment of quantita-tive tactile perceptual specifications by laypersons that are elicited in situational contexts such as auditory-visual-tactile vehicle scenes. Howev-er, such specifications can also be assessed by providing only verbal de-scriptions of the content of these scenes. Quasi identical ratings observed for both presentation modes suggest that tactile user expectations can be quantified even before any vibration is presented. Such expected perceptu-al specifications are the prerequisite for a subsequent translation into phys-ical vibration parameters. Plausibility can be understood as a similarity judgment between elicited features and expected features. Thus, plausible vibration can be synthesized by maximizing the similarity of the elicited perceptual properties to the expected perceptual properties. Based on the observed relationships between vibration parameters and sensory tactile perceptual attributes, a 1-nearest-neighbor model and a regression model were built. The plausibility of the vibrations synthesized by these models in the context of virtual auditory-visual-tactile vehicle scenes was validat-ed in a perceptual study. The results demonstrated that the perceptual spec-ifications obtained with the design language are sufficient to synthesize vibrations, which are perceived as equally plausible as recorded vibrations in a given situational context. Overall, the demonstrated design method can be a new, more efficient tool for designers authoring vibrations for virtual environments or creating tactile feedback. The method enables further automation of the design process and thus potential time and cost reductions.:Preface III Abstract V Zusammenfassung VII List of Abbreviations XV 1 Introduction 1 1.1 General Introduction 1 1.1 Objectives of the Thesis 4 1.2 Structure of the Thesis 4 2. Tactile Perception in Real and Virtual Environments 7 2.1 Tactile Perception as a Multilayered Process 7 2.1.1 Physical Layer 8 2.1.2 Mechanoreceptor Layer 9 2.1.3 Sensory Layer 19 2.1.4 Affective Layer 26 2.2 Perception of Virtual Environments 29 2.2.1 The Place Illusion 29 2.2.2 The Plausibility Illusion 31 2.3 Approaches for the Authoring of Vibrations 38 2.3.1 Approaches on the Physical Layer 38 2.3.2 Approaches on the Mechanoreceptor Layer 40 2.3.3 Approaches on the Sensory Layer 40 2.3.4 Approaches on the Affective Layer 43 2.4 Summary 43 3. Research Concept 47 3.1 Research Questions 47 3.1.1 Foundations of the Research Concept 47 3.1.2 Research Concept 49 3.2 Limitations 50 4. Development of the Experimental Setup 53 4.1 Hardware 53 4.1.1 Optical Reproduction System 53 4.1.2 Acoustical Reproduction System 54 4.1.3 Whole-Body Vibration Reproduction System 56 4.2 Software 64 4.2.1 Combination of Reproduction Systems for Unimodal and Multimodal Presentation 64 4.2.2 Conducting Perceptual Studies 65 5. Assessment of a Sensory Tactile Design Language for Characterizing Vibration 67 5.1.1 Design Language Requirements 67 5.1.2 Method to Assess the Design Language 69 5.1.3 Goals of this Chapter 70 5.2 Tactile Stimuli 72 5.2.1 Generalization into Excitation Patterns 72 5.2.2 Definition of Parameter Values of the Excitation Patterns 75 5.2.3 Generation of the Stimuli 85 5.2.4 Summary 86 5.3 Assessment of the most relevant Sensory Tactile Perceptual Attributes 86 5.3.1 Experimental Design 87 5.3.2 Participants 88 5.3.3 Results 88 5.3.4 Aggregation and Prioritization 89 5.3.5 Summary 91 5.4 Identification of the Attributes forming the Design Language 92 5.4.1 Experimental Design 93 5.4.2 Participants 95 5.4.3 Results 95 5.4.4 Selecting the Elements of the Sensory Tactile Design Language 106 5.4.5 Summary 109 5.5 Summary and Discussion 109 5.5.1 Summary 109 5.5.2 Discussion 111 6. Quantification of Expected Properties with the Sensory Tactile Design Language 115 6.1 Multimodal Stimuli 116 6.1.1 Selection of the Scenes 116 6.1.2 Recording of the Scenes 117 6.1.3 Recorded Stimuli 119 6.2 Qualitative Communication in the Presence of Vibration 123 6.2.1 Experimental Design 123 6.2.2 Participants 124 6.2.3 Results 124 6.2.4 Summary 126 6.3 Quantitative Communication in the Presence of Vibration 126 6.3.1 Experimental Design 127 6.3.2 Participants 127 6.3.3 Results 127 6.3.4 Summary 129 6.4 Quantitative Communication in the Absence of Vibration 129 6.4.1 Experimental Design 130 6.4.2 Participants 132 6.4.3 Results 132 6.4.4 Summary 134 6.5 Summary and Discussion 135 7. Synthesis Models for the Translation of Sensory Tactile Properties into Vibration 137 7.1 Formalization of the Tactile Plausibility Illusion for Models 139 7.1.1 Formalization of Plausibility 139 7.1.2 Model Boundaries 143 7.2 Investigation of the Influence of Vibration Level on Attribute Ratings 144 7.2.1 Stimuli 145 7.2.2 Experimental Design 145 7.2.3 Participants 146 7.2.4 Results 146 7.2.5 Summary 148 7.3 Comparison of Modulated Vibration to Successive Impulse-like Vibration 148 7.3.1 Stimuli 149 7.3.2 Experimental Design 151 7.3.3 Participants 151 7.3.4 Results 151 7.3.5 Summary 153 7.4 Synthesis Based on the Discrete Estimates of a k-Nearest-Neighbor Classifier 153 7.4.1 Definition of the K-Nearest-Neighbor Classifier 154 7.4.2 Analysis Model 155 7.4.3 Synthesis Model 156 7.4.4 Interpolation of acceleration level for the vibration attribute profile pairs 158 7.4.5 Implementation of the Synthesis 159 7.4.6 Advantages and Disadvantages 164 7.5 Synthesis Based on the Quasi-Continuous Estimates of Regression Models 166 7.5.1 Overall Model Structure 168 7.5.2 Classification of the Excitation Pattern with a Support Vector Machine 171 7.5.3 General Approach to the Regression Models of each Excitation Pattern 178 7.5.4 Synthesis for the Impulse-like Excitation Pattern 181 7.5.5 Synthesis for the Bandlimited White Gaussian Noise Excitation Pattern 187 7.5.6 Synthesis for the Amplitude Modulated Sinusoidal Excitation Pattern 193 7.5.7 Synthesis for the Sinusoidal Excitation Pattern 199 7.5.8 Implementation of the Synthesis 205 7.5.9 Advantages and Disadvantages of the Approach 208 7.6 Validation of the Synthesis Models 210 7.6.1 Stimuli 212 7.6.2 Experimental Design 212 7.6.3 Participants 214 7.6.4 Results 214 7.6.5 Summary 219 7.7 Summary and Discussion 219 7.7.1 Summary 219 7.7.2 Discussion 222 8. General Discussion and Outlook 227 Acknowledgment 237 References 237Eines der zentralen Ziele des Designs von Produkten oder virtuellen Um-gebungen ist die Schaffung von Erfahrungen, die im beabsichtigten An-wendungskontext die Erwartungen der Benutzer erfüllen. Gegenwärtig versucht man im vibrotaktilen Authoring-Prozess mit ineffizienten Trial-and-Error-Verfahren, die Erwartungen an den dargestellten, virtuellen Situationskontext implizit zu erfüllen. Ein effizienteres, modellgetriebenes Verfahren, das explizit auf den taktilen Benutzererwartungen basiert, wäre daher von Vorteil. Im Alltag sind wir häufig verschiedenen Ganzkörper-schwingungen ausgesetzt. Abhängig von ihren zeitlichen und spektralen Eigenschaften assoziieren wir intuitiv bestimmte Wahrnehmungsmerkmale wie z.B. “kribbeln”. Dies legt eine systematische Beziehung zwischen physikalischen Parametern und Wahrnehmungsmerkmalen nahe. Um mit potentiellen Nutzern über hervorgerufene oder erwartete taktile Eigen-schaften zu kommunizieren, wird eine standardisierte Designsprache vor-geschlagen. Sie enthält eine Menge von sensorisch-taktilen Wahrneh-mungsmerkmalen, die hinreichend den Wahrnehmungsraum der im Alltag auftretenden Vibrationen charakterisieren. Diese Entwurfssprache ermög-licht die quantitative Beurteilung taktiler Wahrnehmungsmerkmale, die in Situationskontexten wie z.B. auditiv-visuell-taktilen Fahrzeugszenen her-vorgerufen werden. Solche Wahrnehmungsspezifikationen können jedoch auch bewertet werden, indem der Inhalt dieser Szenen verbal beschrieben wird. Quasi identische Bewertungen für beide Präsentationsmodi deuten darauf hin, dass die taktilen Benutzererwartungen quantifiziert werden können, noch bevor eine Vibration präsentiert wird. Die erwarteten Wahr-nehmungsspezifikationen sind die Voraussetzung für eine anschließende Übersetzung in physikalische Schwingungsparameter. Plausible Vibratio-nen können synthetisiert werden, indem die erwarteten Wahrnehmungs-merkmale hervorgerufen werden. Auf der Grundlage der beobachteten Beziehungen zwischen Schwingungs¬parametern und sensorisch-taktilen Wahrnehmungsmerkmalen wurden ein 1-Nearest-Neighbor-Modell und ein Regressionsmodell erstellt. Die Plausibilität der von diesen Modellen synthetisierten Schwingungen im Kontext virtueller, auditorisch-visuell-taktiler Fahrzeugszenen wurde in einer Wahrnehmungsstudie validiert. Die Ergebnisse zeigten, dass die mit der Designsprache gewonnenen Wahr-nehmungsspezifikationen ausreichen, um Schwingungen zu synthetisieren, die in einem gegebenen Situationskontext als ebenso plausibel empfunden werden wie aufgezeichnete Schwingungen. Die demonstrierte Entwurfsme-thode stellt ein neues, effizienteres Werkzeug für Designer dar, die Schwingungen für virtuelle Umgebungen erstellen oder taktiles Feedback für Produkte erzeugen.:Preface III Abstract V Zusammenfassung VII List of Abbreviations XV 1 Introduction 1 1.1 General Introduction 1 1.1 Objectives of the Thesis 4 1.2 Structure of the Thesis 4 2. Tactile Perception in Real and Virtual Environments 7 2.1 Tactile Perception as a Multilayered Process 7 2.1.1 Physical Layer 8 2.1.2 Mechanoreceptor Layer 9 2.1.3 Sensory Layer 19 2.1.4 Affective Layer 26 2.2 Perception of Virtual Environments 29 2.2.1 The Place Illusion 29 2.2.2 The Plausibility Illusion 31 2.3 Approaches for the Authoring of Vibrations 38 2.3.1 Approaches on the Physical Layer 38 2.3.2 Approaches on the Mechanoreceptor Layer 40 2.3.3 Approaches on the Sensory Layer 40 2.3.4 Approaches on the Affective Layer 43 2.4 Summary 43 3. Research Concept 47 3.1 Research Questions 47 3.1.1 Foundations of the Research Concept 47 3.1.2 Research Concept 49 3.2 Limitations 50 4. Development of the Experimental Setup 53 4.1 Hardware 53 4.1.1 Optical Reproduction System 53 4.1.2 Acoustical Reproduction System 54 4.1.3 Whole-Body Vibration Reproduction System 56 4.2 Software 64 4.2.1 Combination of Reproduction Systems for Unimodal and Multimodal Presentation 64 4.2.2 Conducting Perceptual Studies 65 5. Assessment of a Sensory Tactile Design Language for Characterizing Vibration 67 5.1.1 Design Language Requirements 67 5.1.2 Method to Assess the Design Language 69 5.1.3 Goals of this Chapter 70 5.2 Tactile Stimuli 72 5.2.1 Generalization into Excitation Patterns 72 5.2.2 Definition of Parameter Values of the Excitation Patterns 75 5.2.3 Generation of the Stimuli 85 5.2.4 Summary 86 5.3 Assessment of the most relevant Sensory Tactile Perceptual Attributes 86 5.3.1 Experimental Design 87 5.3.2 Participants 88 5.3.3 Results 88 5.3.4 Aggregation and Prioritization 89 5.3.5 Summary 91 5.4 Identification of the Attributes forming the Design Language 92 5.4.1 Experimental Design 93 5.4.2 Participants 95 5.4.3 Results 95 5.4.4 Selecting the Elements of the Sensory Tactile Design Language 106 5.4.5 Summary 109 5.5 Summary and Discussion 109 5.5.1 Summary 109 5.5.2 Discussion 111 6. Quantification of Expected Properties with the Sensory Tactile Design Language 115 6.1 Multimodal Stimuli 116 6.1.1 Selection of the Scenes 116 6.1.2 Recording of the Scenes 117 6.1.3 Recorded Stimuli 119 6.2 Qualitative Communication in the Presence of Vibration 123 6.2.1 Experimental Design 123 6.2.2 Participants 124 6.2.3 Results 124 6.2.4 Summary 126 6.3 Quantitative Communication in the Presence of Vibration 126 6.3.1 Experimental Design 127 6.3.2 Participants 127 6.3.3 Results 127 6.3.4 Summary 129 6.4 Quantitative Communication in the Absence of Vibration 129 6.4.1 Experimental Design 130 6.4.2 Participants 132 6.4.3 Results 132 6.4.4 Summary 134 6.5 Summary and Discussion 135 7. Synthesis Models for the Translation of Sensory Tactile Properties into Vibration 137 7.1 Formalization of the Tactile Plausibility Illusion for Models 139 7.1.1 Formalization of Plausibility 139 7.1.2 Model Boundaries 143 7.2 Investigation of the Influence of Vibration Level on Attribute Ratings 144 7.2.1 Stimuli 145 7.2.2 Experimental Design 145 7.2.3 Participants 146 7.2.4 Results 146 7.2.5 Summary 148 7.3 Comparison of Modulated Vibration to Successive Impulse-like Vibration 148 7.3.1 Stimuli 149 7.3.2 Experimental Design 151 7.3.3 Participants 151 7.3.4 Results 151 7.3.5 Summary 153 7.4 Synthesis Based on the Discrete Estimates of a k-Nearest-Neighbor Classifier 153 7.4.1 Definition of the K-Nearest-Neighbor Classifier 154 7.4.2 Analysis Model 155 7.4.3 Synthesis Model 156 7.4.4 Interpolation of acceleration level for the vibration attribute profile pairs 158 7.4.5 Implementation of the Synthesis 159 7.4.6 Advantages and Disadvantages 164 7.5 Synthesis Based on the Quasi-Continuous Estimates of Regression Models 166 7.5.1 Overall Model Structure 168 7.5.2 Classification of the Excitation Pattern with a Support Vector Machine 171 7.5.3 General Approach to the Regression Models of each Excitation Pattern 178 7.5.4 Synthesis for the Impulse-like Excitation Pattern 181 7.5.5 Synthesis for the Bandlimited White Gaussian Noise Excitation Pattern 187 7.5.6 Synthesis for the Amplitude Modulated Sinusoidal Excitation Pattern 193 7.5.7 Synthesis for the Sinusoidal Excitation Pattern 199 7.5.8 Implementation of the Synthesis 205 7.5.9 Advantages and Disadvantages of the Approach 208 7.6 Validation of the Synthesis Models 210 7.6.1 Stimuli 212 7.6.2 Experimental Design 212 7.6.3 Participants 214 7.6.4 Results 214 7.6.5 Summary 219 7.7 Summary and Discussion 219 7.7.1 Summary 219 7.7.2 Discussion 222 8. General Discussion and Outlook 227 Acknowledgment 237 References 23

Investigating perceptual congruence between information and sensory parameters in auditory and vibrotactile displays

A fundamental interaction between a computer and its user(s) is the transmission of information between the two and there are many situations where it is necessary for this interaction to occur non-visually, such as using sound or vibration. To design successful interactions in these modalities, it is necessary to understand how users perceive mappings between information and acoustic or vibration parameters, so that these parameters can be designed such that they are perceived as congruent. This thesis investigates several data-sound and data-vibration mappings by using psychophysical scaling to understand how users perceive the mappings. It also investigates the impact that using these methods during design has when they are integrated into an auditory or vibrotactile display. To investigate acoustic parameters that may provide more perceptually congruent data-sound mappings, Experiments 1 and 2 explored several psychoacoustic parameters for use in a mapping. These studies found that applying amplitude modulation — or roughness — to a signal, or applying broadband noise to it resulted in performance which were similar to conducting the task visually. Experiments 3 and 4 used scaling methods to map how a user perceived a change in an information parameter, for a given change in an acoustic or vibrotactile parameter. Experiment 3 showed that increases in acoustic parameters that are generally considered undesirable in music were perceived as congruent with information parameters with negative valence such as stress or danger. Experiment 4 found that data-vibration mappings were more generalised — a given increase in a vibrotactile parameter was almost always perceived as an increase in an information parameter — regardless of the valence of the information parameter. Experiments 5 and 6 investigated the impact that using results from the scaling methods used in Experiments 3 and 4 had on users' performance when using an auditory or vibrotactile display. These experiments also explored the impact that the complexity of the context which the display was placed had on user performance. These studies found that using mappings based on scaling results did not significantly impact user's performance with a simple auditory display, but it did reduce response times in a more complex use-case

Learning-Based Reference-Free Speech Quality Assessment for Normal Hearing and Hearing Impaired Applications

Accurate speech quality measures are highly attractive and beneficial in the design, fine-tuning, and benchmarking of speech processing algorithms, devices, and communication systems. Switching from narrowband telecommunication to wideband telephony is a change within the telecommunication industry which provides users with better speech quality experience but introduces a number of challenges in speech processing. Noise is the most common distortion on audio signals and as a result there have been a lot of studies on developing high performance noise reduction algorithms. Assistive hearing devices are designed to decrease communication difficulties for people with loss of hearing. As the algorithms within these devices become more advanced, it becomes increasingly crucial to develop accurate and robust quality metrics to assess their performance. Objective speech quality measurements are more attractive compared to subjective assessments as they are cost-effective and subjective variability is eliminated. Although there has been extensive research on objective speech quality evaluation for narrowband speech, those methods are unsuitable for wideband telephony. In the case of hearing-impaired applications, objective quality assessment is challenging as it has to be capable of distinguishing between desired modifications which make signals audible and undesired artifacts. In this thesis a model is proposed that allows extracting two sets of features from the distorted signal only. This approach which is called reference-free (nonintrusive) assessment is attractive as it does not need access to the reference signal. Although this benefit makes nonintrusive assessments suitable for real-time applications, more features need to be extracted and smartly combined to provide comparable accuracy as intrusive metrics. Two feature vectors are proposed to extract information from distorted signals and their performance is examined in three studies. In the first study, both feature vectors are trained on various portions of a noise reduction database for normal hearing applications. In the second study, the same investigation is performed on two sets of databases acquired through several hearing aids. Third study examined the generalizability of the proposed metrics on benchmarking four wireless remote microphones in a variety of environmental conditions. Machine learning techniques are deployed for training the models in the three studies. The studies show that one of the feature sets is robust when trained on different portions of the data from different databases and it also provides good quality prediction accuracy for both normal hearing and hearing-impaired applications

Effects of training and lung volume levels on voice onset control and cortical activation in singers

Singers need to counteract respiratory elastic recoil at high and low lung volume levels (LVLs) to maintain consistent airflow and pressure while singing. Professionally trained singers modify their vocal and respiratory systems creating a physiologically stable and perceptually pleasing voice quality at varying LVLs. In manuscript 1, we compared non-singers and singers on the initiation of a voiceless plosive followed by a vowel at low (30% vital capacity, VC), intermediate (50%VC), and high (80%VC) LVLs. In manuscript 2, we examined how vocal students (singers in manuscript 1) learn to control their voice onset at varying LVLs before and after a semester of voice training within a university program. Also examined were the effects of training level and LVLs on cortical activation patterns between non-singers and singers (manuscript 1), and within vocal students before and after training (manuscript 2) using fNIRS. Results revealed decreased control of voice onset initially in singers prior to training as compared to non-singers, but significant improvements in initial voice onset control after training, although task difficulty continued to alter voice physiology throughout. Cortical activation patterns did not change with training but continued to show increased activation during the most difficult tasks, which was more pronounced after training. Professionally trained techniques for consistent, coordinated voice initiation were shown to alter voice onset following plosive consonants with training. However, in non-singers and, as performance improved in singers after training, cortical activation remained greatest during the tasks at low LVLs when difficulty was highest

Effects of sound-induced hearing loss and hearing AIDS on the perception of music

This is the final version of the article. It first appeared from the Audio Engineering Society via https://doi.org/10.17743/jaes.2015.0081Exposure to high-level music produces several physiological changes in the auditory system that lead to a variety of perceptual effects. Damage to the outer hair cells within the cochlea leads to a loss of sensitivity to weak sounds, loudness recruitment (a more rapid than normal growth of loudness with increasing sound level) and reduced frequency selectivity. Damage to inner hair cells and/or synapses leads to degeneration of neurons in the auditory nerve and to a reduced flow of information to the brain. This leads to poorer auditory discrimination and may contribute to reduced sensitivity to the temporal fine structure of sounds and to poor pitch perception. Hearing aids compensate for the effects of threshold elevation and loudness recruitment via multi-channel amplitude compression, but they do not compensate for reduced frequency selectivity or loss of inner hair cells/synapses/neurons. Multi-channel compression can impair some aspects of the perception of music, such as the ability to hear out one instrument or voice from a mixture. The limited frequency range and irregular frequency response of most hearing aids is associated with poor sound quality for music. Finally, systems for reducing acoustic feedback can have undesirable side effects when listening to music.This work was supported by the Medical Research Council (UK, grant number G0701870), Action on Hearing Loss, Phonak, and Starkey

Autoencoding sensory substitution

Tens of millions of people live blind, and their number is ever increasing. Visual-to-auditory sensory substitution (SS) encompasses a family of cheap, generic solutions to assist the visually impaired by conveying visual information through sound. The required SS training is lengthy: months of effort is necessary to reach a practical level of adaptation. There are two reasons for the tedious training process: the elongated substituting audio signal, and the disregard for the compressive characteristics of the human hearing system. To overcome these obstacles, we developed a novel class of SS methods, by training deep recurrent autoencoders for image-to-sound conversion. We successfully trained deep learning models on different datasets to execute visual-to-auditory stimulus conversion. By constraining the visual space, we demonstrated the viability of shortened substituting audio signals, while proposing mechanisms, such as the integration of computational hearing models, to optimally convey visual features in the substituting stimulus as perceptually discernible auditory components. We tested our approach in two separate cases. In the first experiment, the author went blindfolded for 5 days, while performing SS training on hand posture discrimination. The second experiment assessed the accuracy of reaching movements towards objects on a table. In both test cases, above-chance-level accuracy was attained after a few hours of training. Our novel SS architecture broadens the horizon of rehabilitation methods engineered for the visually impaired. Further improvements on the proposed model shall yield hastened rehabilitation of the blind and a wider adaptation of SS devices as a consequence