XPod - a human activity and emotion aware mobile music player

Dornbush, S.; Fisher, Kelly; McKay, K.J.; Приходько, А. И.; Segall, Zary

doi:10.1049/cp:20051486

Cited by 32 publications

(25 citation statements)

References 1 publication

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“…However, almost all proposed systems require additional hardware for context logging, e.g. Elliott and Tomlinson (2006), Dornbush et al (2007), and Cunningham et al (2008). Table 1 Categorization of literature about music retrieval including user aspects In Kaminskas and Ricci (2011) a system that matches tags describing a particular place with tags describing music is presented.…”

Section: What About the User In Mir?mentioning

confidence: 99%

The neglected user in music information retrieval research

2013

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Personalization and context-awareness are highly important topics in research on Intelligent Information Systems. In the fields of Music Information Retrieval (MIR) and Music Recommendation in particular, user-centric algorithms should ideally provide music that perfectly fits each individual listener in each imaginable situation and for each of her information or entertainment needs. Even though preliminary steps towards such systems have recently been presented at the "International Society for Music Information Retrieval Conference" (ISMIR) and at similar venues, this vision is still far away from becoming a reality. In this article, we investigate and discuss literature on the topic of user-centric music retrieval and reflect on why the breakthrough in this field has not been achieved yet. Given the different expertises of the authors, we shed light on why this topic is a particularly challenging one, taking computer science and psychology points of view. Whereas the computer science aspect centers on the problems of user modeling, machine learning,

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Section: What About the User In Mir?mentioning

confidence: 99%

The neglected user in music information retrieval research

2013

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“…Then the input image is first checked for the facial features. [7] In case if the image does not contain human features, then it does not detect it. If the input image contains Human features, then it detects the features.…”

Section: 1 Extracting Effective Featuresmentioning

confidence: 99%

Emotion Based Music Player - XBeats

Gupte¹,

Naganarayanan²,

Krishnan³

2016

IJAERS

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“…K.McKay et. al [17] designed xpod-a human activity and emotion aware music player. Their system employed sensors to collect information related to a user's emotions and activities for music recommendation.…”

Section: Literature Surveymentioning

confidence: 99%

An Accurate Algorithm for Generating a Music Playlist based on Facial Expressions

Dureha¹

2014

IJCA

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Manual segregation of a playlist and annotation of songs, in accordance with the current emotional state of a user, is labor intensive and time consuming. Numerous algorithms have been proposed to automate this process. However the existing algorithms are slow, increase the overall cost of the system by using additional hardware (e.g. EEG systems and sensors) and have less accuracy. This paper presents an algorithm that automates the process of generating an audio playlist, based on the facial expressions of a user, for rendering salvage of time and labor, invested in performing the process manually. The algorithm proposed in this paper aspires to reduce the overall computational time and the cost of the designed system. It also aims at increasing the accuracy of the designed system. The facial expression recognition module of the proposed algorithm is validated by testing the system against user dependent and user independent dataset. Experimental results indicate that the user dependent results give 100% accuracy, while user independent results for joy and surprise are 100 %, but for sad, anger and fear are 84.3 %, 80 % and is 66% respectively. The overall accuracy of the emotion recognition algorithm, for user independent dataset is 86%. In audio, 100 % recognition rates are obtained for sad, sad-anger and joy-anger but for joy and anger, recognition rates obtained are 95.4% and 90 % respectively. The overall accuracy of the audio emotion recognition algorithm is 98%. Implementation and testing of the proposed algorithm is carried out using an inbuilt camera. Hence, the proposed algorithm reduces the overall cost of the system successfully. Also, on average, the proposed algorithm takes 1.10 sec to generate a playlist based on facial expression. Thus, it yields better performance, in terms of computational time, as compared to the algorithms in the existing literature.

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XPod - a human activity and emotion aware mobile music player

Cited by 32 publications

References 1 publication

The neglected user in music information retrieval research

The neglected user in music information retrieval research

Emotion Based Music Player - XBeats

An Accurate Algorithm for Generating a Music Playlist based on Facial Expressions

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