I'm seeing unexpected results when examining the results from a sound classification test. Whilst I appear to get accurate startTime for observations, the duration is always the same as the value put into the windowDuration. I'm guessing I'm misunderstanding the purpose of duration in the classification results. The link here says: The time range’s CMTime values are the number of audio frames at the analyzer’s sample rate. Use these time indices to determine where, in time, the result corresponds to the original audio. My understanding of this statement is it should give me the startTime AND the duration of that detection event. For example, if I attempt to detect a crowd sound and that sound lasts for 1.8 seconds, then I should see 1.8 seconds in the duration. Below is some code showing what I'm seeing. Initialisation of request.windDuration of 1 second. If I change this to any other value, that value is reported back as the duration of the event. Even if the event is half a second in duration. Any help in either a code issue or understanding the results better would be appreciated. Thanks let request = try SNClassifySoundRequest(classifierIdentifier: .version1) request.overlapFactor = 0.8 request.windowDuration = CMTimeMakeWithSeconds(600, preferredTimescale: 600) My code to get the values out of the SNResult func request(_ request: SNRequest, didProduce result: SNResult) { guard let analysisResult = result as? SNClassificationResult, let predominantSound = analysisResult.classifications.first?.identifier, soundsToDetect.contains(predominantSound) else { return } let startTime = analysisResult.timeRange.start.seconds let duration = analysisResult.timeRange.duration.seconds let confidence = analysisResult.classifications.first?.confidence ?? 0.0 let detectedSound = ClassificationObject(id: UUID(), name: predominantSound, startTime: startTime, duration: duration, confidence: confidence) self.detectedSounds.append(detectedSound) }

Hello! I am working on the final thesis of my Bachelor Degree, and I would like to focus on podcasts. I found an interesting dataset on Kaggle which includes reviews of iTunes podcasts and the link to the podcast. I would be interested in analysing the audio tracks to extract some features like pitch and intensity. The number of links is about 10,000. Is there any tool I could use to extract such features from the tracks? Thank you very much for your time :)

i have a MacBook Pro 15' 2015mid. I use with an audio interface (soundcraft ui 24r). it has 32 Chanels. 10-out 22in puts. when I want play something audio in safari, it is mono. But when I play example spotfy, iTunes, are stereo. what could be the problem? how could I change safari audio to stereo?

Hi, We are developing a hearing test that runs on apple devices. As part of the process of getting our product certified risk factors are to be assessed. One risk factor can be that the device malfunction and sends a sound (constant high) that ultimately damages the test persons hearing. Is anyone familiar with the hight possible output of sound (in dB) from apple devices?

Hi Community Members, We tried to find the particular sound through the microphone. We used PKCCheck to detect the sound decibel. we detect the sound based on the number of dB values per second we receive and add some logic over it to get the result, but when we have continuous sound like an alarm we can't detect it as the gap between the sound is very less. Any suggestion on libraries to achieve this. Also whether we can achieve this thru Frequency & Amplitude method. Please advise.