Ching-Hua Chuan

• PhD
• Research Associate at University of Miami

This article discusses promising avenues for integrating affective computational approaches into advertising research on emotion. We review affective computing methods for different modalities (text, visual, and audio) and present advertising research examples and computational tools for each modality. We discuss different state-of-the-art multimod...

This study developed and evaluated a theory-driven mobile app to facilitate pro-environmental behavior change and habit formation to address the prevalent attitude-behavior gap in environmental activism. The app first informs users of interconnected environmental problems and offers various pro-environmental behaviors for users to practice daily. T...

Healthcare is one the fastest growing fields for conversational agents (CAs), also known as chatbots. Compared with traditional human-machine interfaces which focus on utilitarian features, chatbots offer unique advantage in understanding the user’s intent and acquiring critical information from the user via natural communications. More importantly...

To advocate for corporate digital responsibility (CDR) in the era of artificial intelligence (AI), this article provides a comprehensive overview of AI in the advertising process as well as the associated ethical and sociopolitical concerns. We first define AI and explain the key concepts and approaches in creating AI systems, before we discuss the...

This roundtable focuses on the interplay between artificial intelligence (AI) and advertising, and its broader societal implications. The discussion first provides a historical overview of AI and then moves to AI's relevance to the advertising industry, highlighting tools like programmatic advertising and generative AI. It then covers the definitio...

Objective: Recruiting diverse participants for precision medicine (PM) research programs should overcome low literacy and varied expectations. Information aids (IA) can address these barriers through patient-centered education. The purpose of this study was to evaluate the effectiveness of three information aids (IA) on participating in PM. Metho...

Chatbots, also known as conversational agents, are automated computer programs powered by natural language processing designed to engage consumers in interactive, one-on-one, personalized text- or voice-based conversations. Focusing on text-based, anthropomorphic social chatbots that can be easily implemented on various digital platforms, this chap...

This study illuminates the varied emotional mechanisms underlying consumer response to ads paired with emotionally congruent versus incongruent content in different placement positions. This work expands the media planning literature that has narrowly focused on thematic (in)congruency. Focusing on music videos, Study 1 empirically tests the affect...

Based on the theoretical framework of agency effect, this study examined the role of affect in influencing the effects of chatbot versus human brand representatives in the context of health marketing communication about HPV vaccines. We conducted a 2 (perceived agency: chatbot vs. human) × 3 (affect elicitation: embarrassment, anger, neutral) betwe...

By applying the computational method of decision trees, this research identifies the most decisive attributes enhancing ad persuasiveness by examining the contextual effects of emotional (in)congruence on ad placement for music videos on YouTube. Findings of this interdisciplinary research not only evaluated key psychological constructs via a compu...

Purpose This study presents one of the earliest empirical investigations on how brand chatbots' anthropomorphic design and social presence communication strategies may improve consumer evaluation outcomes via the mediators of parasocial interaction and perceived dialogue. Design/methodology/approach This study employs a 2 (high vs. low social pres...

Clinical trials are important tools to improve knowledge about the effectiveness of new treatments for all diseases, including cancers. However, studies show that fewer than 5% of cancer patients are enrolled in any type of research study or clinical trial. Although there is a wide variety of reasons for the low participation rate, we address this...

This study presents one of the earliest empirical studies that evaluate the effects of different interactivity and message design aspects of augmented reality (AR) advertising on consumer response. Specifically, this research examined whether and how AR interaction type (i.e., instrumental vs. hedonic), ad context (i.e., realistic vs. imaginative),...

The growing trend of politically motivated consumer boycotts and buycotts on social media not only impacts a company’s financial bottom line, but more fundamentally disrupts relationships between the firm and its publics, the cornerstone of public relations (Ferguson, 1984; Sommerfeldt, & Kent, 2015). On a broader level, such politically motivated...

We explore the potential of a popular distributional semantics vector space model, word2vec, for capturing meaningful relationships in ecological (complex polyphonic) music. More precisely, the skip-gram version of word2vec is used to model slices of music from a large corpus spanning eight musical genres. In this newly learned vector space, a metr...

Publics' perceptions of new scientific advances such as AI are often informed and influenced by news coverage. To understand how artificial intelligence (AI) was framed in U.S. newspapers, a content analysis based on framing theory in journalism and science communication was conducted. This study identified the dominant topics and frames, as well a...

Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with referen...

We explore the potential of a popular distributional semantics vector space model, word2vec, for capturing meaningful relationships in ecological (complex polyphonic) music. More precisely, the skip-gram version of word2vec is used to model slices of music from a large corpus spanning eight musical genres. In this newly learned vector space, a metr...

We explore the potential of a popular distributional semantics vector space model, word2vec, for capturing meaningful relationships in ecological (complex poly-phonic) music. More precisely, the skip-gram version of word2vec is used to model slices of music from a large corpus spanning eight musical genres. In this newly learned vector space, a met...

We propose an end-to-end approach for modeling polyphonic music with a novel graphical representation, based on music theory, in a deep neural network. Despite the success of deep learning in various applications, it remains a challenge to incorporate existing domain knowledge in a network without affecting its training routines. In this paper we p...

We propose an end-to-end approach for modeling polyphonic music with a novel graphical representation, based on music theory, in a deep neural network. Despite the success of deep learning in various applications, it remains a challenge to incorporate existing domain knowledge in a network without affecting its training routines. In this paper we p...

Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with referen...

Emotional response to music is often represented on a two-dimensional arousal-valence space without reference to score information that may provide critical cues to explain the observed data. To bridge this gap, we present IMMA-Emo, an integrated software system for visualising emotion data aligned with music audio and score, so as to provide an in...

We present a semantic vector space model for capturing complex polyphonic musical context. A word2vec model based on a skip-gram representation with negative sampling was used to model slices of music from a dataset of Beethoven’s piano sonatas. A visualization of the reduced vector space using t-distributed stochastic neighbor embedding shows that...

Proceedings of the First International Workshop on Deep Learning and Music joint with IJCNN in Anchorage, Alaska (May 18-19, 2017). There has been tremendous interest in deep learning across many fields of study. Recently, these techniques have gained popularity in the field of music. Projects such as Magenta (Google's Brain Team's music generati...

Musicologists, music cognition scientists and others have long studied music in all of its facets. During the last few decades, research in both score and audio technology has opened the doors for automated, or (in many cases) semi-automated analysis. There remains a big gap, however, between the field of audio (performance) and score-based systems...

This paper describes an interactive mobile application that aims to assist children who are deaf or hard of hearing (D/HH) and their families to learn and practice American Sign Language (ASL). Approximately 95% of D/HH children are born to hearing parents. Research indicates that the lack of common communication tools between the parent and child...

This paper proposes and tests models that provide quick searching and retrieval of continuations and the longest repeated suffix for data sequences, particularly musical data, using relational databases. The authors extend existing interactive music-generation systems by focusing on large input sequences. Algorithms for indexing prefix trees and fa...

This paper presents a pilot study on an intelligent tutoring system for domain-independent argument making. Students' responses to an open-ended question were collected as the instances for supervised text classification based on the grade given by the instructor using structured outcome of the learning observation taxonomy. The responses were proc...

This paper proposed and tested a model that provides quick search and retrieval of continuations for time series, particularly musical data, using relational databases. The model extends an existing interactive music-generation system by focusing on large input sequences. Experiments using textural and musical data provided satisfactory performance...

In this paper, we present an American Sign Language recognition system using a compact and affordable 3D motion sensor. The palm-sized Leap Motion sensor provides a much more portable and economical solution than Cyblerglove or Microsoft kinect used in existing studies. We apply k-nearest neighbor and support vector machine to classify the 26 lette...

In this paper, we present a benchmark dataset based on the KUSC classical music collection and provide baseline key-finding comparison results. Audio key finding is a basic music information retrieval task; it forms an essential component of systems for music segmentation, similarity assessment, and mood detection. Due to copyright restrictions a...

Data mining tasks such as music indexing, information retrieval, and similarity search, require an understanding of how listeners process music internally. Many algorithms for automatically analyzing the structure of recorded music assume that a large change in one or another musical feature suggests a section boundary. However, this assumption has...

In this paper, the authors use statistical models to predict the difficulty of recognizing musical keys from polyphonic audio signals. The key recognition difficulty provides important background information when comparing the performance of audio key finding algorithms that often evaluated using different private data sets. Given an audio recordin...

In this paper, we present statistical models to predict the difficulty of recognizing musical keys from polyphonic audio signals. Automatic audio key finding has been studied for many years, and various approaches have been proposed and reported. Reports of these methods' performance are usually based on the proposers' own data sets. Without detail...

This paper presents a multimodal approach to style identification in pop/rock music. Considering the intuitive feelings of similarity from the listener's perspective, this study focuses on features that are computed using similarity metrics for melodies, harmonies, and audio signals for style identification. Support vector machine is used as a bina...

Audio key finding is an integral step in content-based music indexing and retrieval. In this paper, we present a system that combines ensemble learning with an existing model-based key finding algorithm: the Fuzzy Analysis Center of Effect Generator algorithm. We demonstrate the manner in which AdaBoost improves the accuracy of FACEG using a datase...

This paper presents an audio classification and retrieval system using wavelets for extracting low-level acoustic features. The author performed multiple-level decomposition using discrete wavelet transform to extract acoustic features from audio recordings at different scales and times. The extracted features are then translated into a compact vec...

In this paper, we present an audio classification system using wavelets for extracting low-level acoustic features. We perform multiple-level decomposition using Discrete Wavelet Transform to extract acoustic features at different scales and time from audio recordings. The extracted features are then translated into a compact vector representation....

In this article we present a hybrid approach to the design of an automatic, style-specific accompaniment system that combines statistical learning with a music-theoretic framework, and we propose quantitative methods for evaluating the results of machine-generated accompaniment. The system is capable of learning accompaniment style from sparse inpu...

N-gram models have been successfully applied to harmonic analysis for differentiating a composer's style based on all the pieces in a large corpus of the composer. In this paper, we focus on each individual song and explore the effectiveness of the n-gram model when it is applied to a different but equally important musical task: harmonic style-bas...

The process of generating chords for harmonizing a melody with the goal of mimicking an artist's style is investigated in this paper. We compared and tested three different approaches, including a rule-based model, a statistical model, and a hybrid system of the two, for such tasks. Experiments were conducted using songs from seven stylistically id...

We present a human-centered experiment designed to measure the degree of support for creating musical accompaniment provided by an interactive composition decision- support system. We create an interactive system with visual and audio cues to assist users in the choosing of chords to craft an accompaniment in a desired style. We propose general mea...

I describe a method for analyzing pop-rock musical styles by extracting chord patterns based on their structural roles in relation to melody and lyrics. Previous work applied models such as n-grams, treating chords as equally chopped fragments without considering their functions with respect to musical structures like phrases. In this paper I focus...

In this paper, we described an automatic style-specific accompaniment system and an interactive user interface designed to support creative music composition. With the use of both music theoretical knowledge and statistical learning, the system provides users with an easy start by suggesting a refined accompaniment based on the examples given by us...

We propose general quantitative methods for evaluating and visualizing the results of machine-generated style-specific accompaniment. The evaluation of automated accompani- ment systems, and the degree to which they emulate a style, has been based primarily on subjective opinion. To quan- tify style similarity between machine-generated and orig- in...

ABSTRACT We present an approach to phrase segmentation that starts with an expressive music performance. Previous research has shown,that phrases are delineated by tempo speedups and slowdowns. We propose a dynamic,programming,al- gorithm for extracting phrases from tempo,information. We test two hypotheses for modeling phrase tempo shapes: a quadr...

Creating distinctive harmonizations in an identifiable style may be one of the most difficult tasks for amateur song writers, a novel and acceptable melody being relatively easier to produce; and this difficulty may result in the abandonment of otherwise worthwhile projects. To model and assist in this creative process, we propose a hy-brid system...

We systematically analyze audio key finding to determine factors important to system design, and the selection and evaluation of solutions. First, we present a basic system, fuzzy analysis spiral array center of effect generator algorithm, with three key determination policies: nearest-neighbor (NN), relative distance (RD), and average distance (AD...

In this paper, we explore the effect of musical context on audio onset detection using machine learning techniques. We extract the signal intensity and frequency energy of audio as the attributes of input instances for the machine learning techniques. The audio is synthesized from MIDI files, providing exact information of onset events. We test thr...

Key finding is an integral step in content-based music indexing and retrieval. In this paper, we present an O(n) real-time algorithm for determining key from polyphonic audio. We use the standard Fast Fourier Transform with a local maximum detection scheme to extract pitches and pitch strengths from polyphonic audio. Next, we use Chew's Spiral Arra...

This paper presents a fuzzy analysis technique for pitch class determination that improves the accuracy of key finding from audio information. Errors in audio key finding, typically incorrect assignments of closely related keys, commonly result from imprecise pitch class determination and biases introduced by the quality of the sound. Our technique...

Mobile ad hoc networks have gained more and more research attention. They provide wireless communications without location limitations and pre-built fixed infrastructures. Because of the absence of any static support structure, ad hoc networks are prone to link failure. This has become the most serious cause of throughput degradation when using TCP...

Our key finding system consists of a series of O(n) real-time algorithms for determining key from polyphonic audio. The system comprises of two main parts as shown in Figure 1 [1]. The first part (the upper dashed box) generates pitch class information from audio using the standard FFT and a fuzzy analysis technique. The second component (the lower...

This paper explores the effect musical context, namely key and tempo, on audio onset detection using machine learning techniques, with a focus on the changes in perfor-mance caused by mismatched key and tempo between training and test pieces, and the potential benefits of incorporating such musical information. We extract frequency en-ergy informat...