6. Bit Depth - Digital Audio Fundamentals 

Thank you for your confidence, though I may not share the same views 😅

@@BenCaesar thanks very much mate! A bit self deprecating about being a producer though. That's stuff ain't easy.

Really glad you think so! More on the way.

Thanks for checking it out mate!

Thanks for the feedback mate!

That's great to know that these are helpful! I struggled with audio terminologies as well at the beginning.

Thanks so much!

Sweet! Thanks for checking it out!

Thanks very much! All the best with your finals!

Below 4 bits: Lowest resolution (even more error, like large raining). 8 bits: Lower resolution (more error, white noise). Above 16 bits to 24 bits: Typically the more resolution (no error, much clearer audio).

Thanks you very much! :) I'm glad these videos helped

Excellent suggestion! I have floating point bit depth on my to-do list!

Thanks for checking it out!

Thank you for checking out the series, and letting me know! You are totally right, i should've said 'invert' or 'switch polarity' when describing the subtraction process. Phase shifting to achieve subtraction would only work on simple sinusoids.

Haha! It was atrocious at loud volumes! 😂 Thanks mate!

Most casettes and top quality tapes were sounding around that range

Thanks very much!

Glad you liked it!

Thanks! And you're welcome.

You're welcome! :)

Cheers! Thanks for checking it out.

@@akashmurthy Can you do a video on explaining 32 Bit float separately ?

@@rohanbenny8632 That's a good point. I'll add information about 32 bit floats when I make a video on Headroom. Thanks!

Thanks for watching!

Thanks very much. Yes, Sreejesh has been very kindly sharing this.

Thanks for checking it out! I'm always on the lookout for a better, more intuitive way of explaining a concept as well! Most of the animations are done on After Effects. I'm curious to see your videos.

Thanks very much for checking out the series! Yes, I want to do those topics in the future, but I have a few other topics that I feel like I need to get to before that..!

Cheers mate!

Hey, thanks! Interesting suggestion. I didn't know there were linear phase EQ specifically designed to combat this problem. I'm going to be doing a module on filters later on. Most filters we use today are IIRs (Infinite impulse response). But to maintain the same phase relationship we would need a FIR (finite impulse response) filter, which are computationally more expensive. I'll be sure to address your question there. But I'm not sure how long it'll take to get there!

Thanks so much! :) I'm glad you find these useful!

I've mentioned a part of this in a comment on an earlier video. Consider this scenario, you have 2 monitors connected to your mixer, one is a really small low powered monitor, and another is a large stage monitor. If you boost the gain on your mixer by +6dbFS, the pressure level measured from each of these monitors are vastly different. So there is no easy relationship between an arbitrary reference level (dBFS) and physical pressure (dBSPL). It'll have to be dependent on the speaker source. dBFS is not measuring loudness. It's a reference level when producing music. It is used for comparing levels of different instruments. I can say that vocals are at 0dBFS, and guitars are 10dBFS below that. That doesn't mean anything in terms of SPL. If you play this recording loudly, you can have this play at 80dBSPL, or you can play it at low volume at 40dBSPL. The only thing you can say with some accuracy is the relative levels. So in the mixed track, if vocals are played at 80dBSPL, then guitars appear at 70dBSPL, similarly if vocals are played at 40dbSPL, the guitars appear at 30dBSPL. The relative dBFS level and relative dBSPL values can match (with an ideal speaker). So when I'm talking about SQNR here, I'm talking about the dynamic range - a range of values from the maximum signal level that can be represented to the minimum signal level before it merges into noise. This is relative! The higher the bit depth, the higher the RANGE between the 2 levels. So, if you took a 16 bit recording, which fully utilises the entire dynamic range, and you play it back, lets say at around 100dBSPL (there is no relationship here, I'm just cranking up the volume till the measuring instrument records a peak of 100dBSPL). All I was saying was the noise from quantization would be present at 4dBSPL, since 16bit has 96db SQNR, a relative range between the highest possible level and low level noise. Hope that helps.

Haha, thanks for checking it out! I don't believe I'm a professional audio technician by any means! But thanks for your confidence..

Bit depth wouldn't cause aliasing as such, it would only introduce quantisation error. It would make it sound noisy or even introduce inharmonic distortion at times (similar to aliasing). But fundamentally, the way the noise is formed or added is different for aliasing distortion vs quantisation distortion through low bit depth.

@@akashmurthy that makes sense. Back in the 90's we would seek out that crunch in the samples we used in our music. We liked the patina. While we called it "aliasing" at the time, we probably were hearing quantization distortion. Good stuff. Love this series.

Adobe After Effects

Logic gates perhaps.

It's actually + or - 2^(16 - 1) So, + or - 32768 But generally, in programming, anywhere outside final delivery, audio sample points are handled in floating point precision ( + 1.0 to - 1.0 )

Great question! So, 24bits of "fixed point" audio is all you need when you're playing back audio, when you don't have to manipulate the audio in anyway and just play back the audio as it is. Usually consumer audio is never stored in a bit depth greater than 24bit, because of the reason you mentioned - there is no need for it. But when you need to do signal processing on the audio, for example: when you want to change the gain, apply filtering, or convolution, or any other type of effects, you have to do mathematical operations on the audio data. If you're manipulating audio data stored in "fixed point" format, each mathematical operation, like multiplication or division has a little bit of error associated with it. This is because of the nature of how data is represented in fixed point format, and the limited precision associated with it. With n-order filtering, the signal is fed back into the input, several times, and this error could accumulate over time. This could result in potentially more noise. Because of this, audio programmers - both DSP hardware and computer software programmers use "floating point" representation of data. Because of how it is represented, the floating point data has inherent mechanisms to cope with error, the accumulation of error is minimised. And according to the IEEE standard, a floating point number is generally 32bits or 64bits. And that's why, when you're recording audio into your DAW software, the DAW software will almost always represent your audio data in 32bit float or 64bit float, and never usually in fixed point representation. I'm working on videos at the moment regarding floating point Vs fixed point data, and their pros and cons. Hopefully I can finish it soon.

@@akashmurthy So cleaaar ! Thank you so much for the time you take to answer to my question ! I’m definitely impressed by the quality of your work bro ! 🙏🙏

Without principles, we are animals.