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Daily 'Digital'

Ideas for a better living in the increasingly digital world!
by Praveen Baratam
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Recently our digital displays have gone from being mere information displays to high fidelity retina-esque magic mirrors but our ears are still waiting to be pampered by our digital companions.

Yes, Apple iPhones and some recent Androids carry some of the best Digital to Analog Converters (DACs) around and may satisfy the audiophiles among us but the progress is kind of sluggish compared to the display front.

Why do we still rely on analog audio output (3.5mm jack) from our Smartphone/Player? Simplicity is a good enough reason but how do we best this approach? These analog interfaces are known to reduce fidelity and limit us to the inbuilt DAC. Why not use a digital interface such as SPDIF, Optical, HDMI, MHL, etc.?

MHL looks like an apt choice for this! It supports high-def multi-channel audio, carries commands back and forth, shares the same port with USB and even provides power for our external DAC and amplifier. Wonderful!

This way we can upgrade to a better DAC and eliminate the distortion from analog connectors to start with! However, the possibilities are endless and this approach opens up a new arena for innovation and engineering perfection. Imagine multi-channel 3D headphones! :-)
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Everyday millions of people use their gadgets to play music and its one of the main reasons for purchasing these gadgets in the first place. This music playing experience has evolved by leaps and bounds in the recent years with due emphasis on eye candy and intuitive interfaces but hasn't garnered any significant innovation per se from the days of Winamp. All players sans few more or less do the same thing when we are actually waiting for smarter players and services.

One aspect of our music apps that we really depend on is its 'Shuffle' functionality. We really wish our apps could play DJ for us and delight us with an interesting mix but not much is happening on this front. Some apps did try though. For example, MixZing (Android) senses the mood of a chosen track and plays similar tracks for us from our library. Kudos to MixZing and other similar apps for that!

Nevertheless, I wish we had a smarter Shuffle function on our music players; one that can filter songs on various criteria such as mood, date added, date released, artist, etc. How about mixing and playing tracks that match the mood of a chosen track from the tracks that were recently added to our library? Sounds cools isn't it! Similarly, we will rejoice at the idea of listening to a to mix of latest releases or old releases (if we are feeling nostalgic) with or without mood filtering!
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Hypothetical 'Shuffle' options for playing old tracks with 'Mood Tracking'
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Praveen Baratam, Maven - 1 year ago Edit
@Nicholas - Not much is known about Mood Tracking since its proprietary technology. But I read some where that factors such as Beats Per Minute (BPM), tempo, energy levels/distribution, frequency distribution, etc. were used to infer mood. Download MixZing if you have an android and give it a try! Its not perfect but certainly not dumb either. Coming to 'Smart Shuffle', except mood tracking which is kind of digital alchemy for now, weighting and filtering on particular info is perfectly doable.
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Jeffrey Harrison White, Contributor - 9 months ago Edit
How about this for an added feature. The "mood tracking" feature scrapes social data (status updates, types of news read/articles shared) to tailor a playlist to your anticipated mood.
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Praveen Baratam, Maven - 9 months ago Edit
@Jeffrey - Sounds great in theory! But inferring mood from social signals may be more complicated than we think! Computers can classify music and play tracks from one or more inferred groups but cannot attach such groups to a particular situation/mood in the real world. Ex. It can play tracks similar to - Maroon 5 - One more night - but cannot pick that track when you are in the mood to dance. Contd...
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Praveen Baratam, Maven - 9 months ago Edit
Contd... For that we first need to build associative intelligence through observation (what tracks were you listening to when you were in a similar mood - inferred from similar social signals - the previous few times) so that they have few tracks to start with and follow! Computers do not understand our language or the real world! All they can do is associate and correlate data and this particular feature falls into the realm of AI.
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