This is a well-studied problem, dating back from the mid 90s (DARPA/NIST broadcast transcription challenges). Search for "speech/music segmentation" or "audio segmentation" and you'll find thousands of research papers.
There are two broad approaches to solve this problem:
Train a speech/music classifier, using a standard machine ...
What you want to do is called "Super Resolution" in the field of imaging.
This is an ill posed problem.
Hence in order to solve it you need some prior / model about your audio data.
For example you may look at the model in Speech Super Resolution Using Parallel WaveNet.
While I don't know about tools for upsampling using AI, I am challenging the assumption that the main problem with your sample is lost high frequncies due to resampling from 44kHz to 22kHz, so "just" upsampling is unlikely to solve your root problem.
The possibility of AI upsampling itself is sensible, though: While standard (mathematical) ...
The i-vectors and x-vectors share the ability to represent speech utterance in a compact way (as a vector of fixed size, regardless of length of the utterance).
The extraction algorithms of i-vectors and x-vector are quite different. The x-vector concept is newer and the name of the method is similar to "i-vector" to suggests that this representation can be ...
Researchers from the Johns Hopkins University have recently released a corpus of music, speech, and noise which, according to them, is suitable for training models for voice activity detection and music/speech discrimination.
See https://arxiv.org/pdf/1510.08484.pdf for details.
Depending on the actual recordings, the algorithm complexity could range from dead easy to really complex...
I'll take the studio recording case first, so I can assume :
- (Almost) no noise coming from outside (cars, trucks, bus...)
- Nobody slamming the door in the middle of the recording
- Voice samples are recorded at optimal level independently of who ...
You can take some big speech corpus like TEDLIUM and add the noise you like:
The advantage of TEDLIUM is that it's a set of continuous recordings with speech timings, not just a collection of utterances.
It's better to copy first frame and last frame values to extend vector sequence beyond boundaries than to assign 0. This could be implemented just by adjusting indexes:
if (index1 < 0)
index1 = 0
if (index2 > N - 1)
index2 = N - 1
delta = v[index1] - v[index2]
Dynamic Time Warping is pretty well explained on this site. I'll use some of the diagrams from the PPT on that site to explain.
The idea is to divide the signals into segments (frames) and then compare frames sequentially through each signal. As illustrated below, motion from a segment in one signal to the next segment depends on the similarity to the ...
What you are observing is the digital representation of the voltage, which in fact represents the acoustic pressure.
Workflow would be something like:
Vibrating larynx is producing Acoustic Pressure [Pa]
Variations of that pressure are converted by the microphone into Voltage [V]
Voltage is being sampled and quantized by the ADC (Analog to Digital Converter)...
Yes, the cellular phones use various forms of compression to convert the captured analog audio (speech) into the digital bitstream for transmission through 2G/3G/.../.
The specific method used depends on the GSM version which might dictate its own bandwidth constraint and backward or forward compatibility issues into the audio encoding stage.
Band-pass filtering with cut-off frequencies of 300 Hz and 3400 Hz should result in a good approximation. Try with a Chebychev filter or order not more than 6.
Then you may need to downsample your audio to 8000 samples per second, which is the standard for telephony.
P.S. The actual cut-off frequencies (especially the 3400 Hz) may be different according to ...
As pointed in some of the comments, there are several issues involved in your problem that you should consider to go for an optimal solution:
Quality of the record system. Features of the microphone, filter of the preamps, etc. What is the frequency response of the system (mic + preamp + adc)? https://blog.faberacoustical.com/2009/ios/iphone/iphone-...
Audio watermarking is a technique that relies on inaudible cues. Humans can't really hear phase differences in low tones, if only because it's often delivered via a single bass speaker (i.e. because the HiFi setup physically eliminates the difference). This allows you to stuff information in the phases of the low-frequency parts of the left and right channel....
Yes, this should be enough for a basic isolated word recognition system. Probably not something for a commercial product, but good enough for a university project or demo...
It would be better to ask the user to record a word and match against this, rather than attempt to match against a large database of utterances of the same word by different speakers. ...
I think you have a misunderstanding about formants, pitch and partial tones. The spectrogram shows the position of the partials and you can also estimate their fundamental frequency, or pitch, trajectory to be the position of the lowest partial. The formant structure is not readily recognizable from this representation.
Formants are the resonances of the ...
An offset of 20 samples is added as 8 lines above (auto=autocor(21:160)) this offset is cut away from the autocorrelation sequence. In the line you mention, this offset has therefore to be considered.
This is done in order to cut away the first maximum (at time lags around zero) and avoid pitch errors caused by picking a delay value that does not make sense ...
I will not address the question whether or not it is meaningful to compute the instantaneous frequency of a speech signal. Instead I will show you a better method for computing the instantaneous frequency from a given analytic signal. This method avoids the phase unwrapping problem by directly computing the instantaneous frequency from the real and imaginary ...
Ok so here is what I found. The distance is dependent on the way that the mfccs are calculated. This makes sense to me and also explains why cepstral mean normalization affects the values of the MCD.
I found this implementation (https://github.com/MattShannon/mcd), which unfortunately did not support .wav files. I ran this and it gives results that are in ...
I have no access to your audio files so I've downloaded:
IR from here (mono/r1_omni.wav) - it's a really long one
Anechoic recording from here (operatic-voice/mono/singing.wav)
Resampled voice signals:
Final convolved signal:
As for your questions:
As you did the plot of IR in logarithmic scale it's clearly visible that towards its end there is ...
Different flavors of cross correlation are probably the best choice here. It really depends on how exactly the recordings are different. Two microphones side by side behave very differently than two microphones in different spots in the room. Your best method will depend on a clear understanding of what your application needs to cover and what doesn't.
You compute the energy (before the log operation) as
with some small constant $\epsilon$ (they suggest $\epsilon=2e-22$ which gives $\ln\epsilon\approx -50$) to prevent the energy to become too small. The reason for this is that the logarithm of zero is $-\infty$ and gives you numerical trouble. So you ...
Dan Ellis has some neat Matlab scripts that allow you to pull audio features from files . . .
The dynamic time warp might be a good starting point for the task you describe.
system.Telephone is just for detection of speech, that's why you get only init clusters. You can use default fr.lium.spkDiarization.system.Diarization it will do proper steps as described in LIUM docs, result will be like this:
;; cluster S0
file 1 0 1135 M S U S0
file 1 5722 300 M S U S0
file 1 9266 359 M S U S0
file 1 12670 370 M S U S0
;; cluster S1
The 1st mentioned tutorial https://email@example.com/dynamic-range-compression-for-audio-with-ffmpeg-and-compand-621fe2b1a892 is a great introduction and the filter documentation also adds a few examples.
Its very dependent on situation if your set up for near field or far field but generally its ...
I just came across one article related to the compand command that might be useful (just in case someone looking for help sees this article).
From the article:
To test this, and my understanding of compand, I added a very simple filter to remove the quiet section of the audio by decreasing their volume dramatically:
ffmpeg -i in.mp3 -filter_complex \
Will that affect the quality of speech comparison, and by how much?