6 ms·

I've gone the opposite route. After years of careful curating my music library, I started to feel chained to the past, listening to a handful of tracks over and

by Bakary 9y ago

I've gone the opposite route. After years of careful curating my music library, I started to feel chained to the past, listening to a handful of tracks over and over. Since music is connected to strong emotions, this would also bleed into other aspects of my life and cause me to be less forward-thinking. I'm fully aware that Spotify and its competitors are deals with the Devil but if they allow me to feel less burdened, the price is worth it. It's deeply hypocritical but I also secretly hope that at least a few people stick to archiving and curating just in case.

logfromblammo 9y ago
I trained a NI expert system on the kind of music I like, and turned it loose on the Internet, where it uses the Amazon wishlist API to make recommendations. In other words, I had a kid, played my favorite music to the baby, and can now mooch off all the wonderful new CDs that show up in the house. All CDs get ripped and encoded as FLAC for the family media drive, and everyone transcodes their own lossy files for their own portable devices. It's kind of an expensive solution, though, and occasionally fails to recommend music that I like.
- aoeuasdf1 9y ago
  Can you post code for this somewhere?
  - logfromblammo 9y ago
    It has been around forever, and sufficiently detailed practical demonstrations are all over the network--often flagged with the acronym NSFW, which stands for "Natural Sapience Field Work".~
    - Natanael_L 9y ago
      You left out the training system details for the neural network.
      - logfromblammo 9y ago
        The NI is loaded into a biomechanical interface that provides sensory inputs, locomotive actuators, and environmental manipulators. Typically, only one researcher builds the entire device, and collaboration is not useful for that part of the project. (Unfortunately, as the network heavily exploits subtle implementation details in the mechanicals, it is very difficult to perform upgrades after the first stages of training are completed.) From there, the researcher has to continually upload conceptual primitives through the sensory apparatus, and the NI prunes and rebalances its own neural network to establish basic foundation concepts such as object permanence, the acceleration of gravity, thermodynamics, ballistic path prediction, etc. Eventually, when the network is sufficiently trained, researchers may begin to input additional data through a natural language interface. Due to variations in the biomechanical devices, it is currently impossible to use standard bootstrap code to accelerate that process. In order for the NI to be useful as a music recommendations engine, it is essential to expose it to music that you already like through its audio sensors, during the initial training phases. After approximately 8 years, the NI will begin to autonomously seek out music samples in the wild and recommend that you purchase copies of promising collections. The system is not perfect. It will occasionally issue recommendations for music that was already present in the training corpus, or for maliciously-formed music files designed to hack uninoculated NIs into recommending them. And it should be noted that these NIs have been known to abruptly diverge from preferences implied by the training corpus, producing wildly inaccurate recommendations thereafter. It's probably just cheaper and more reliable to use an AI, but as long as this thing still works okay, I'm going to keep refueling it.
Obi_Juan_Kenobi 9y ago
I embrace both. I still manage an mp3 library and use a (modded) iPod because network access is unreliable for me sometimes. It's a curated library that I spend time adding to and culling from; it's its own little hobby. I also really like streaming services. I like to be able to press a couple buttons and get a non-stop stream of music that I don't have to worry about, and can often lead to new artists, etc. They each have their pros and cons; I see no reason to be dogmatic about either. The separation also helps with curation; I can keep social and casual music to the streaming stuff, and keep the library focused on 'dedicated' listening.
K0SM0S 9y ago
This is what happened to me as well. I used to spend several hours per week on average looking for new music (new for me, might be old releases) and Spotify's AI basically does the job for me now. Well worth 10 bucks per month. Also, the biggest factor in "sound quality" is the matching of the master and the device used to listen (most notably compression of the dynamic range, you want it strong on low-end speakers and/or noisy environments, and as dynamic as possible on high-end systems in good listening conditions). I'm very pleased with Spotify's masters, they're often equal or superior to the CD even, which is nice in an otherwise "loudness war" ("bricked") market (been that way sadly since the late 90's, which brings tears to many a sound engineer). I can't speak for Apple Music (never tried it) but Google Play Music has absolutely terrible masters in many cases, way too compressed, sounding flat. Note: I speak of dynamic range compression (audio technique), which has nothing to do with data compression (e.g. MP3). The actual end-user codec has actually little to nothing to do with it, it's all about mastering, i.e. targeted reproduction devices and listening conditions.

by Bakary 9y ago

logfromblammo 9y ago

I trained a NI expert system on the kind of music I like, and turned it loose on the Internet, where it uses the Amazon wishlist API to make recommendations. In other words, I had a kid, played my favorite music to the baby, and can now mooch off all the wonderful new CDs that show up in the house. All CDs get ripped and encoded as FLAC for the family media drive, and everyone transcodes their own lossy files for their own portable devices. It's kind of an expensive solution, though, and occasionally fails to recommend music that I like.

aoeuasdf1 9y ago
Can you post code for this somewhere?
- logfromblammo 9y ago
  It has been around forever, and sufficiently detailed practical demonstrations are all over the network--often flagged with the acronym NSFW, which stands for "Natural Sapience Field Work".~
  - Natanael_L 9y ago
    You left out the training system details for the neural network.
    - logfromblammo 9y ago
      The NI is loaded into a biomechanical interface that provides sensory inputs, locomotive actuators, and environmental manipulators. Typically, only one researcher builds the entire device, and collaboration is not useful for that part of the project. (Unfortunately, as the network heavily exploits subtle implementation details in the mechanicals, it is very difficult to perform upgrades after the first stages of training are completed.) From there, the researcher has to continually upload conceptual primitives through the sensory apparatus, and the NI prunes and rebalances its own neural network to establish basic foundation concepts such as object permanence, the acceleration of gravity, thermodynamics, ballistic path prediction, etc. Eventually, when the network is sufficiently trained, researchers may begin to input additional data through a natural language interface. Due to variations in the biomechanical devices, it is currently impossible to use standard bootstrap code to accelerate that process. In order for the NI to be useful as a music recommendations engine, it is essential to expose it to music that you already like through its audio sensors, during the initial training phases. After approximately 8 years, the NI will begin to autonomously seek out music samples in the wild and recommend that you purchase copies of promising collections. The system is not perfect. It will occasionally issue recommendations for music that was already present in the training corpus, or for maliciously-formed music files designed to hack uninoculated NIs into recommending them. And it should be noted that these NIs have been known to abruptly diverge from preferences implied by the training corpus, producing wildly inaccurate recommendations thereafter. It's probably just cheaper and more reliable to use an AI, but as long as this thing still works okay, I'm going to keep refueling it.

Obi_Juan_Kenobi 9y ago

I embrace both. I still manage an mp3 library and use a (modded) iPod because network access is unreliable for me sometimes. It's a curated library that I spend time adding to and culling from; it's its own little hobby. I also really like streaming services. I like to be able to press a couple buttons and get a non-stop stream of music that I don't have to worry about, and can often lead to new artists, etc. They each have their pros and cons; I see no reason to be dogmatic about either. The separation also helps with curation; I can keep social and casual music to the streaming stuff, and keep the library focused on 'dedicated' listening.

K0SM0S 9y ago

This is what happened to me as well. I used to spend several hours per week on average looking for new music (new for me, might be old releases) and Spotify's AI basically does the job for me now. Well worth 10 bucks per month. Also, the biggest factor in "sound quality" is the matching of the master and the device used to listen (most notably compression of the dynamic range, you want it strong on low-end speakers and/or noisy environments, and as dynamic as possible on high-end systems in good listening conditions). I'm very pleased with Spotify's masters, they're often equal or superior to the CD even, which is nice in an otherwise "loudness war" ("bricked") market (been that way sadly since the late 90's, which brings tears to many a sound engineer). I can't speak for Apple Music (never tried it) but Google Play Music has absolutely terrible masters in many cases, way too compressed, sounding flat. Note: I speak of dynamic range compression (audio technique), which has nothing to do with data compression (e.g. MP3). The actual end-user codec has actually little to nothing to do with it, it's all about mastering, i.e. targeted reproduction devices and listening conditions.