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I have a fairly large collection of audio/video bootlegs recorded at live performances by various artists. The recordings all come from very different sources and people through decades, and all of the recordings in the collection are just audio/video files. Since the collection is large, the total running length of the collection is probably hundreds hours.

The quality of each recording varies: some of the recordings sound excellent (let's call it "10"), but some of them sound more like noise ("1") remaining a point of interest for a particular artists collector. Let me say this way, by "quality" I mean something the way "how good" it looks and sounds to me or anyone else (and not the audio/video codecs settings the recordings are encoded or compressed with; and not how a particular artist performs -- it's a matter of personal taste, not audio/video quality). When I started collecting bootlegs more than a decade ago, I remember, I could find characteristics for many of them like "Quality: A+" (really nice), or "Quality: B-" (not that bad, but makes interest to a collector.)

For example:

  1. if a live recording sounds as if it is recorded in a studio, then it might be evaluated to "9" or even "10" (regardless if either a lossy or a loseless audio-codec used);

  2. if a recording shows up noticeable visual VHS artifacts like blue/red/green stripes (just because the recording was recorded on a tape), but the overall picture is pretty good, it might be evaluated say to "5+" up to "7";

  3. if the recording sounds very "bassy" and low frequencies heavily prevail the high ones, it might be evaluated to "3-" since the audio might be considered very low quality, etc. If such a thing exists, I guess it might also to be applicable for audio, video and images;

  4. and more...

Is it possible to analyze a recording in a software way, not listening/viewing to it, to "determine"/"evaluate" its subjective quality?

This question looks pretty much similar to Analyzing the quality of a music track (and probably audio quality evaluation ) , but can't really tell how close it is.

(Please note I have zero knowledge in this area, may use wrong terms and may ask something unreal. The only reason I'm trying to find it out is editing the files metadata by putting the "quality" tags into it, thus evaluating the average/overall quality of the entire collection not spending weeks of listening or watching to all of the recordings regardless hardware I might use. Also not sure if the question is better to ask at Software Recommendations or Sound Design though.)

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Is it possible to analyze a recording in a software way ... to "determine"/"evaluate" its subjective quality

Yes, very possible; all one needs is to define, mathematically, what "good quality" is - and enough data. The full pipeline may involve:

  1. Understanding basic signal processing decomposition of audio (i.e. building blocks) - see DSP Guide
  2. Extract relevant features with a transform, e.g. wavelet scattering, time-frequency scattering (both implemented at Kymatio), synchrosqueezing, MFCC, etc.
  3. Apply learning algorithm with suitable objective function:
    • The function can be a measure of "distance" in feature space. This can be done by having a "template" for what's considered "good music" based on extracted features.
    • Test phase can involve direct subjective assessment of test subjects, as in Natural Language Processing - but the optimization function must be entirely mathematical.
    • Success requires sufficient data. The requirement is lowered with better feature engineering and transfer learning (e.g. NLP transformers).

I'm not familiar with any specific software, nor am I an expert in this field - but I know of a few experts; worth asking here.

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  • $\begingroup$ "all one needs is to define, mathematically, what 'good quality' is" - To me this sounds as it's underestimating the fundamental challenge of the task drastically. There have been numerous attempts to find a mathematical description of perceived quality - with good results, that, however, in my opinion still are far from optimal. This is taking into account that most of the developed methods are even based on knowledge of a reference signal, to which the perceived quality of a test signal is compared. $\endgroup$
    – applesoup
    Aug 26 at 18:11
  • $\begingroup$ @applesoup Agreed, it's much easier said than done. But the challenge is similar to NLP; perhaps with equal research effort we'd get same or better results. We already have AI-composed music, for one - presumably there's far less interest in music evaluation systems. $\endgroup$ Aug 27 at 6:32
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I think if there was really very much to the latest "AI" craze, doing what you want here would be a piece of cake. I don't think it's doable at all. Good luck

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