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Novelty measures as cues for temporal salience in audio similarity 新颖性测量作为音频相似性时间显著性的线索
MIRUM '12 Pub Date : 2012-11-02 DOI: 10.1145/2390848.2390862
M. Cartwright, Bryan Pardo
{"title":"Novelty measures as cues for temporal salience in audio similarity","authors":"M. Cartwright, Bryan Pardo","doi":"10.1145/2390848.2390862","DOIUrl":"https://doi.org/10.1145/2390848.2390862","url":null,"abstract":"Most algorithms for estimating audio similarity either completely disregard time or they treat each moment in time equally. However, many studies over the years have noted several factors that affect how much attention we give to certain sounds or parts of sounds (e.g. loudness, the attack, novelty). These findings suggest that some time segments of audio may be more salient than others when making similarity judgments. We believe that if we could estimate this information, we could improve audio similarity measures. This paper presents the results of a human subject study designed to test the hypothesis that sounds segments with high timbral change are more salient than segments with low timbral change. We then investigate whether we can use this information to improve two audio similarity measures: a \"bag-of-frames\" approach and a dynamic time warping approach.","PeriodicalId":199844,"journal":{"name":"MIRUM '12","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116147490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Fast intra-collection audio matching 快速集合内音频匹配
MIRUM '12 Pub Date : 2012-11-02 DOI: 10.1145/2390848.2390850
Verena Thomas, Sebastian Ewert, M. Clausen
{"title":"Fast intra-collection audio matching","authors":"Verena Thomas, Sebastian Ewert, M. Clausen","doi":"10.1145/2390848.2390850","DOIUrl":"https://doi.org/10.1145/2390848.2390850","url":null,"abstract":"The general goal of audio matching is to identify all audio extracts of a music collection that are similar to a given query snippet. Over the last years, several approaches to this task have been presented. However, due to the complexity of audio matching the proposed approaches usually either yield excellent matches but have a poor runtime or provide quick responses albeit calculate less satisfying retrieval results. In this paper, we present a novel procedure that combines the positive aspects and efficiently computes good retrieval results. Our idea is to exploit the fact that in some practical applications queries are not arbitrary audio snippets but are rather given as extracts from the music collection itself (intra-collection query). This allows us to split the audio collection into equal sized overlapping segments and to precompute their retrieval results using dynamic time warping (DTW). Storing these matches in appropriate index structures enables us to efficiently recombine them at runtime. Our experiments indicate a significant speedup compared to classical DTW-based audio retrieval while achieving nearly the same retrieval quality.","PeriodicalId":199844,"journal":{"name":"MIRUM '12","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131473972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Exploring the relationship between categorical and dimensional emotion semantics of music 探讨音乐的范畴情感语义与维度情感语义的关系
MIRUM '12 Pub Date : 2012-11-02 DOI: 10.1145/2390848.2390865
Ju-Chiang Wang, Yi-Hsuan Yang, Kaichun K. Chang, H. Wang, Shyh-Kang Jeng
{"title":"Exploring the relationship between categorical and dimensional emotion semantics of music","authors":"Ju-Chiang Wang, Yi-Hsuan Yang, Kaichun K. Chang, H. Wang, Shyh-Kang Jeng","doi":"10.1145/2390848.2390865","DOIUrl":"https://doi.org/10.1145/2390848.2390865","url":null,"abstract":"Computational modeling of music emotion has been addressed primarily by two approaches: the categorical approach that categorizes emotions into mood classes and the dimensional approach that regards emotions as numerical values over a few dimensions such as valence and activation. Being two extreme scenarios (discrete/continuous), the two approaches actually share a unified goal of understanding the emotion semantics of music. This paper presents the first computational model that unifies the two semantic modalities under a probabilistic framework, which makes it possible to explore the relationship between them in a computational way. With the proposed framework, mood labels can be mapped into the emotion space in an unsupervised and content-based manner, without any training ground truth annotations for the semantic mapping. Such a function can be applied to automatically generate a semantically structured tag cloud in the emotion space. To demonstrate the effectiveness of the proposed framework, we qualitatively evaluate the mood tag clouds generated from two emotion-annotated corpora, and quantitatively evaluate the accuracy of the categorical-dimensional mapping by comparing the results with those created by psychologists, including the one proposed by Whissell & Plutchik and the one defined in the Affective Norms for English Words (ANEW).","PeriodicalId":199844,"journal":{"name":"MIRUM '12","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127647145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 17
When music, information technology, and medicine meet 当音乐、信息技术和医学相遇
MIRUM '12 Pub Date : 2012-11-02 DOI: 10.1145/2390848.2390859
Ye Wang
{"title":"When music, information technology, and medicine meet","authors":"Ye Wang","doi":"10.1145/2390848.2390859","DOIUrl":"https://doi.org/10.1145/2390848.2390859","url":null,"abstract":"From Napster to YouTube and iTunes, music has always been a major driving force of Internet technologies. A huge amount of music content is now accessible to the public. Organizing and categorizing this content to support an effective recommendation system has become a significant challenge. The primary goal of our lab is to develop new technologies to address this challenge in the field of healthcare. We seek to harness the synergy of sound and music computing (SMC), mobile computing, and cloud computing technologies to promote healthy lifestyles and to facilitate disease prevention, diagnosis, and treatment in both developed countries and resource-poor developing countries. In this talk, I present a collaborative research project between the SMC lab at National University of Singapore and the Music, Neuroimaging, and Stroke Recovery Lab at Beth Israel Deaconess Medical Center (BIDMC) / Harvard Medical School. We are developing a cloud-based therapy delivery system that uses music to enhance limb function and speech in patients with neurological impairments using smart devices such as iPhone. Our focus is to develop high-tech, low-cost solutions that aim to (1) facilitate recovery in patients with post-stroke speech and motor impairments, (2) improve gait and mobility and reduce fall risk in patients with Parkinson's disease (PD), and thereby, improve Quality of Life (QoL) for both patients and caregivers.","PeriodicalId":199844,"journal":{"name":"MIRUM '12","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133000355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Personalized music emotion classification via active learning 基于主动学习的个性化音乐情感分类
MIRUM '12 Pub Date : 2012-11-02 DOI: 10.1145/2390848.2390864
Dan Su, Pascale Fung
{"title":"Personalized music emotion classification via active learning","authors":"Dan Su, Pascale Fung","doi":"10.1145/2390848.2390864","DOIUrl":"https://doi.org/10.1145/2390848.2390864","url":null,"abstract":"We propose using active learning in a personalized music emotion classification framework to solve subjectivity, one of the most challenging issues in music emotion recognition (MER). Personalization is the most direct method to tackle subjectivity in MER. However, almost all of the state-of-the-art personalized MER systems require a huge amount user participation, which is a non-neglegible problem in real systems. Active learning seeks to reduce human annotation efforts, by automatically selecting the most informative instances for human relabeling to train the classifier. Experimental results on a Chinese music dataset demonstrate that our method can effectively reduce as much as 80% of the requirement of human annotation without decreasing F-measure. Different query selection criteria of active learning were also investigated, and we found that informativeness criterion which selects the most uncertain instances performed best in general. We finally show the condition of successful active learning in personalized MER is that label consistency from the same user.","PeriodicalId":199844,"journal":{"name":"MIRUM '12","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132144688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 14
Building a personalized audio equalizer interface with transfer learning and active learning 建立一个个性化的音频均衡器接口与迁移学习和主动学习
MIRUM '12 Pub Date : 2012-11-02 DOI: 10.1145/2390848.2390852
Bryan Pardo, David Little, D. Gergle
{"title":"Building a personalized audio equalizer interface with transfer learning and active learning","authors":"Bryan Pardo, David Little, D. Gergle","doi":"10.1145/2390848.2390852","DOIUrl":"https://doi.org/10.1145/2390848.2390852","url":null,"abstract":"Potential users of audio production software, such as audio equalizers, may be discouraged by the complexity of the interface and a lack of clear affordances in typical interfaces. In this work, we create a personalized on-screen slider that lets the user manipulate the audio with an equalizer in terms of a descriptive term (e.g. \"warm\"). The system learns mappings by presenting a sequence of sounds to the user and correlating the gain in each frequency band with the user's preference rating. This method is extended and improved on by incorporating knowledge from a database of prior concepts taught to the system by prior users. This is done with a combination of active learning and simple transfer learning. Results on a study of 35 participants show personalized audio manipulation tool can be built with 10 times fewer interactions than is possible with the baseline approach.","PeriodicalId":199844,"journal":{"name":"MIRUM '12","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122259313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 25
Who influence the music tastes of adolescents?: a study on interpersonal influence in social networks 谁影响青少年的音乐品味?社会网络中人际影响的研究
MIRUM '12 Pub Date : 2012-11-02 DOI: 10.1145/2390848.2390857
Audrey Laplante
{"title":"Who influence the music tastes of adolescents?: a study on interpersonal influence in social networks","authors":"Audrey Laplante","doi":"10.1145/2390848.2390857","DOIUrl":"https://doi.org/10.1145/2390848.2390857","url":null,"abstract":"Research on music information behavior demonstrates that people rely primarily on others to discover new music. This paper reports on a qualitative study aiming at exploring more in-depth how music information circulates within the social networks of late adolescents and the role the different people involved in the process play. In-depth interviews were conducted with 19 adolescents (15-17 years old). The analysis revealed that music opinion leaders showed eagerness to share music information, tended to seek music information on an ongoing basis, and were perceived as being more knowledgeable than others in music. It was found that the ties that connected participants to opinion leaders were predominantly strong ties, which suggests that trustworthiness is an important component of credibility. These findings could potentially help identify new avenues for the improvement of music recommender systems.","PeriodicalId":199844,"journal":{"name":"MIRUM '12","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122291406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
Two systems for automatic music genre recognition: what are they really recognizing? 两种自动音乐类型识别系统:它们真正识别的是什么?
MIRUM '12 Pub Date : 2012-11-02 DOI: 10.1145/2390848.2390866
Bob L. Sturm
{"title":"Two systems for automatic music genre recognition: what are they really recognizing?","authors":"Bob L. Sturm","doi":"10.1145/2390848.2390866","DOIUrl":"https://doi.org/10.1145/2390848.2390866","url":null,"abstract":"We re-implement two state-of-the-art systems for music genre recognition, and closely examine their behavior. First, we find specific excerpts each system consistently and persistently mislabels. Second, we test the robustness of each system to spectral adjustments to audio signals. Finally, we expose the internal genre models of each system by testing if human can recognize the genres of music excerpts composed by each system to be highly genre-representative. Our results suggest that, though they have high mean classification accuracies, neither system is recognizing music genre.","PeriodicalId":199844,"journal":{"name":"MIRUM '12","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131602358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 32
Perceptual tempo estimation using GMM-regression 基于gmm回归的知觉速度估计
MIRUM '12 Pub Date : 2012-11-02 DOI: 10.1145/2390848.2390861
G. Peeters, Joachim Flocon-Cholet
{"title":"Perceptual tempo estimation using GMM-regression","authors":"G. Peeters, Joachim Flocon-Cholet","doi":"10.1145/2390848.2390861","DOIUrl":"https://doi.org/10.1145/2390848.2390861","url":null,"abstract":"Most current tempo estimation algorithms suffer from the so-called octave estimation problems (estimating twice, thrice, half or one-third of a reference tempo). However, it is difficult to qualify an error as octave error without a clear definition of what is the reference tempo. For this reason, and given that tempo is mostly a perceptual notion, we study here the estimation of perceptual tempo. We consider the perceptual tempo as defined by the results of the large-scale experiment made at Last-FM in 2011. We assume that the perception of tempo is related to the rate of variation of four musical attributes: the variation of energy, of harmonic changes, of spectral balance and short-term-event-repetitions. We then propose the use of GMM-Regression to find the relationship between the perceptual tempo and the four musical attributes. In an experiment, we show that the estimation of the tempo provided by GMM-Regression over these attributes outperforms the one provided by a state-of-the-art tempo estimation algorithm. For this task GMM-Regression also largely outperforms SVM-Regression. We finally study the estimation of three perceptual tempo classes (\"Slow\", \"In Between\", \"Fast\") using both GMM-Regression and SVM-Classification.","PeriodicalId":199844,"journal":{"name":"MIRUM '12","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128250530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 28
Inferring personal traits from music listening history 从听音乐的历史中推断个人特征
MIRUM '12 Pub Date : 2012-11-02 DOI: 10.1145/2390848.2390856
Jen-Yu Liu, Yi-Hsuan Yang
{"title":"Inferring personal traits from music listening history","authors":"Jen-Yu Liu, Yi-Hsuan Yang","doi":"10.1145/2390848.2390856","DOIUrl":"https://doi.org/10.1145/2390848.2390856","url":null,"abstract":"Nowadays, we often leave our personal information on the Internet without noticing it. People could learn things about you from these information. It has been reported that it is possible to infer some personal information from the web browsing records or from blog articles. As the music streaming services become increasingly popular, the music listening history of one person could be acquired easily. This paper investigates the possibility for a computer to automatically infer personal traits such as gender and age from the music listening history. Specifically, we consider three types of features for building the machine learning models, including 1) statistics of the listening timestamps, 2) song/artist metadata, and 3) song signal features, and evaluate the accuracy of binary age classification and gender classification utilizing a 1K-user dataset obtained from the online music service Last.fm. Our study brings about new insights into the human behavior of music listening, but also raises concern over the privacy issues involved in music streaming services.","PeriodicalId":199844,"journal":{"name":"MIRUM '12","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115458139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 20
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