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1

I think you will find a very good explanation in the next link: https://voicefoundation.org/health-science/voice-disorders/anatomy-physiology-of-voice-production/understanding-voice-production/ Cheers.


2

Males usually have deeper voices than females because they have longer vocal folds. Yes Short and relaxed vocal folds produce a deep voice, long and tense vocal folds produce a high-pitched voice. Yes on "relaxed", no on "short". Treating the vocal cords as strings: The resonance frequency is a function of mass (or density), tension ...


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You need to add delay to the mics: double delay = position * sin(angleRad) / speedSound + micdelay[i];


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The spectrum appears to be the noise-shaping associated with Delta-Sigma Modulation. Specifically we see that the noise increase follows a 20 dB/decade slope, indicative of a first-order delta-sigma modulator. The high end of the noise rolls off consistent with the high frequency roll-off of the filter applied. Below are some charts I have explaining the ...


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That's the causal (comes after the impulse) and anticausal part.


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Per VVT's answer: check your filter coefficients. They should be b = 0.98763 -4.9381 9.8763 -9.8763 4.9381 -0.98763 a = 1 -4.9751 9.9007 -9.8515 4.9013 -0.97541 A 5th order highpass at such a low frequency has poles that are very close to the unit circle, so it's vulnerable to numberical ...


0

There are methods, sw and hw that are explicitly designed for this purpose. You would expect to get more robust results per second of measurement using those. If what you have is a recording of the stimulus and the response, you could use an adaptive filter to identify the system response. If the stimulus contains deep nulls or you have lots of room noise, ...


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Passive speaker or active speaker? If it's passive, then it's easy. If it's active, then you need to pull the transducer out and treat it as a passive speaker. If you have a signal generator, use it as a source to generate white noise, otherwise use Matlab script. If you are looking for impedance response, put your speaker in a voltage divider circuit (in ...


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For digital filters, Wn are in the same units as fs. By default, fs is 2 half-cycles/sample, so these are normalized from 0 to 1, where 1 is the Nyquist frequency. (Wn is thus in half-cycles / sample.) For analog filters, Wn is an angular frequency (e.g. rad/s). As yours is an analog filter, your Wn parameter is 2π·54Hz from scipy import constants ... Wn = 2*...


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It seems like you’ve by and large got it, but I did notice a couple things. You’ll probably want to include support for windowing and the corresponding correction factor as shown by APs article on FFT noise. Rectangular windows aren’t great for RMS type measurements (at least in my opinion) for all the side lobe type explanations that are common with ...


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In this case, usually, Normalization is done. For example in your training and testing data, you need the same shape, so that you should try something like, mean = np.mean(X_train_features, axis=0) std = np.std(X_train_features, axis=0) X_train_features = (X_train_features - mean)/std Here X_train_features can be a data frame of spectrogram or mfcc features....


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