Choosing music based on (cadence | temperature | mood)
Choosing music based on (cadence | temperature | mood)
Steve Backer, Nicholas Kong, Ian Leighton
i262 Midterm Project Proposal
October 3, 2011
Introduction
Many people listen to music while going about their daily lives, such as during commuting, exercising, cooking, or for leisure. The environment can influence the sort of music that people like to listen to: for example, slower, more melancholy songs may suit cold, rainy days, while upbeat songs may suit sunny summer days. As another example, music with a large dynamic-range (e.g., classical or jazz recordings) is more suitable for quiet environments, while music with a more compressed range (e.g., pop or rock) is more suitable for noisier environments. We propose a portable system that will select music based on environmental attributes, such as weather reports, ambient light / sound conditions, and temperature. In addition, users can provide input to the system either implicitly (e.g, via their walking pace), or explicitly (by body movements).
Prior work
There are a number of existing applications that attempt to recommend music that is similar to an input artist or track (e.g., Last.fm, Pandora, iTunes Genius playlists). Although this allows users to create playlists of similar songs, the creation process is unaffected by physical inputs, and these systems are not designed to adapt to a user’s surroundings.
There are also some applications and products that are targeted towards runners. Upbeat Workouts [3] is an iPhone application that infers the user’s cadence and selects songs with a tempo that matches that cadence. Oliver and Flores-Mangas [2] and an Apple patent [1] describe similar applications. Our system may take pace as an input, but we also propose using other environmental factors, such as temperature, to allow for more generalized use than exercise.
Example Usage Scenarios
Temperature: Colder temperature may cause music with a more minor tonality to be played, while a hotter temperature might trigger music with strong major chords and melodies. Alternatively, users may specify sets of songs to play in certain circumstances, e.g., a playlist of rainy-day songs, or a playlist for a sunny day.
Light: If there is less ambient light, “sadder” music is played, whereas if there is more light, “happier” music is played — in this sense of “sad” or “happy”, songs may be categorized both by both musical characteristics (tone, timbre, instrumentation, key, etc) or lyrical characteristics.
Sound: Ambient sound might be used in a social context to choose music based on the volume or stress in conversation.
Physical input: Pace or bodily movements (e.g., tapping feet, clapping hands, nodding head), determined via FSR or accelerometer, may provide a third analog input to drive the tempo (BPM) of selected songs. Thus, using this TUI while sitting at an afternoon picnic would call up an entirely different playlist of potential songs than would taking a swift, winter-evening walk.
References
[1] Bowen, A. Music synchronization arrangement. US Patent US 2006/0107822 A1. 2004.
[2] Oliver, N. and Flores-Mangas, F. MPTrain: a mobile, music and physiology-based personal trainer. In Proc. MobileHCI '06. pp. 21-28.
[3] Upbeat Workouts. http://upbeatworkouts.com.
Steve Backer, Nicholas Kong, Ian Leighton
i262 Midterm Project Proposal
October 3, 2011
Introduction
Many people listen to music while going about their daily lives, such as during commuting, exercising, cooking, or for leisure. The environment can influence the sort of music that people like to listen to: for example, slower, more melancholy songs may suit cold, rainy days, while upbeat songs may suit sunny summer days. As another example, music with a large dynamic-range (e.g., classical or jazz recordings) is more suitable for quiet environments, while music with a more compressed range (e.g., pop or rock) is more suitable for noisier environments. We propose a portable system that will select music based on environmental attributes, such as weather reports, ambient light / sound conditions, and temperature. In addition, users can provide input to the system either implicitly (e.g, via their walking pace), or explicitly (by body movements).
Prior work
There are a number of existing applications that attempt to recommend music that is similar to an input artist or track (e.g., Last.fm, Pandora, iTunes Genius playlists). Although this allows users to create playlists of similar songs, the creation process is unaffected by physical inputs, and these systems are not designed to adapt to a user’s surroundings.
There are also some applications and products that are targeted towards runners. Upbeat Workouts [3] is an iPhone application that infers the user’s cadence and selects songs with a tempo that matches that cadence. Oliver and Flores-Mangas [2] and an Apple patent [1] describe similar applications. Our system may take pace as an input, but we also propose using other environmental factors, such as temperature, to allow for more generalized use than exercise.
Example Usage Scenarios
Temperature: Colder temperature may cause music with a more minor tonality to be played, while a hotter temperature might trigger music with strong major chords and melodies. Alternatively, users may specify sets of songs to play in certain circumstances, e.g., a playlist of rainy-day songs, or a playlist for a sunny day.
Light: If there is less ambient light, “sadder” music is played, whereas if there is more light, “happier” music is played — in this sense of “sad” or “happy”, songs may be categorized both by both musical characteristics (tone, timbre, instrumentation, key, etc) or lyrical characteristics.
Sound: Ambient sound might be used in a social context to choose music based on the volume or stress in conversation.
Physical input: Pace or bodily movements (e.g., tapping feet, clapping hands, nodding head), determined via FSR or accelerometer, may provide a third analog input to drive the tempo (BPM) of selected songs. Thus, using this TUI while sitting at an afternoon picnic would call up an entirely different playlist of potential songs than would taking a swift, winter-evening walk.
References
[1] Bowen, A. Music synchronization arrangement. US Patent US 2006/0107822 A1. 2004.
[2] Oliver, N. and Flores-Mangas, F. MPTrain: a mobile, music and physiology-based personal trainer. In Proc. MobileHCI '06. pp. 21-28.
[3] Upbeat Workouts. http://upbeatworkouts.com.
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