fileFormat: AVAudioFormat the format of the audio file itself.Let inputFile: AVAudioFile = try! AVAudioFile (forReading: inputFileURL)Īfter doing so, we can take a look at 2 important properties of the opened file: Preparationįirst, we have to open the input file for reading, get some information about it, and open the output file for writing. Theoretically, it’s possible to copy the lossy audio data directly, like mp3DirectCut does, but this is also a thing to figure out yourself. It’s possible to use AVAudioConverter to convert the audio to the desired format, but this is out of the scope of this article.Īlso, keep in mind that multiple conversions from lossy to lossy format (like MP3 or AAC) will degrade the audio quality. However, as later turned out, the header of the output file matched the set format, but the actual audio data had the format of the internal processing with AVFoundation (32-bit float WAV). While working on implementing this functionality, I was sure that AVFoundation converts the data to the set format of the output file on the fly. Fortunately, it was completely possible with AVAudioFile – it can be read into AVAudioPCMBuffer and written from one’s contents. That was exactly what we needed – to open the input file for reading, the output one for writing, calculate the range of audio samples to copy, and perform the actual copying. Secondly, I took a look at the requirements again. My first guess was to use AVAssetExportSession, but it has limited options for exporting the audio, and it’s not possible to be sure what it does with the audio under the hood. There should be an ability to add silence to the output file.No processing should be applied to the audio data, audio samples should be copied as-is.The output file should have the exact same format as the input file.No matter, go forth and classify sounds free of charge thanks to the fine work from Cupertino & Friends™. Instead, Apple has advanced so far up the Machine Learning tree that it was barely a footnote. The fact that something like this exists, and I just simply happened to stumble upon it while doc divin’ on Apple’s developer portal just goes to show that…what a freakin’ crazy time to be a developer, right? I think that if this shipped, say, even five years ago - it might’ve been the talk of the town. These days I can erase my dross attempts at becoming a machine learning expert because…Apple has, in many cases, already done the work for me. Look at Apple’s demo project built in SwiftUI to see how to swing that. Public enum SNTimeDurationConstraint įurther, if you don’t have a set audio file, there is also an object for streaming audio and finding things on the fly. As such, to query it, use the extension Apple has given it off of SNClassifierIdentifier: As I mentioned in the lede, there’s one such classifier that ships on-device. All it needs is the identifier of a classifier to use. If you’re curious which sounds you can detect with the classifier, you can query them all using SNClassifySoundRequest. Sound Analysis’ engineers must’ve listened to its developer audience, because looking at it from the start, the framework sounds easy to get started with – yielding only 8-10 top level objects to check out 1: Apple has made it 20 lines of code, give or take, to classify those sounds. So if you don’t know your neaural networks from your decision trees, you’re in luck. While I still feel like a novice when it comes to…uh, anything, machine learning related - this framework has done a solid for us all by including a sound classifier that ships right with the framework that can detect over 300 sounds: To that end, I stumbled upon the Sound Analysis framework a few weeks ago and was impressed at its breadth and depth. However, just beyond things meant for us code wranglers, we can’t ignore that iOS seems to make makes millions of choices each day based off of what custom models conjure up: Siri handling requests, a watch detecting a fall or your iPhone’s mic automatically picking up sounds in the environment. Is it just me, or does Apple seem to roll out more machine learning advancements with nearly every new OS release lately? Sure, CreateML was a big one - but that’s a developer facing tool.
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