Every state in our original estimate could have moved to arangeof states. Because we like Gaussian blobs so much, we’ll say that each point in \(\color{royalblue}{\mathbf{\hat{x}}_{k-1}}\) is moved to somewhere inside a Gaussian blob with covariance \(\color{mediumaquamarine}{\mathb...
The noise-canceling microphone A4Tech HS-12o was used to record speech in the following format: PCM wav, mono, sampling rate 8000 Hz, 16 bits per sample. The signal-noise ratio (SNR) is ap- proximately equal to 30 dB. To simplify the experiment, we require the speaker to pronounce ...
Every state in our original estimate could have moved to arangeof states. Because we like Gaussian blobs so much, we’ll say that each point in \(\color{royalblue}{\mathbf{\hat{x}}_{k-1}}\) is moved to somewhere inside a Gaussian blob with covariance \(\color{mediumaquamarine}{\mathb...
Every state in our original estimate could have moved to arangeof states. Because we like Gaussian blobs so much, we’ll say that each point in \(\color{royalblue}{\mathbf{\hat{x}}_{k-1}}\) is moved to somewhere inside a Gaussian blob with covariance \(\color{mediumaquamarine}{\mathb...
If we have two probabilities and we want to know the chance thatbothare true, we just multiply them together. So, we take the two Gaussian blobs and multiply them: What we’re left with is theoverlap, the region wherebothblobs are bright/likely. And it’s a lot more precise than eith...
If we have two probabilities and we want to know the chance thatbothare true, we just multiply them together. So, we take the two Gaussian blobs and multiply them: What we’re left with is theoverlap, the region wherebothblobs are bright/likely. And it’s a lot more precise than eith...
If we have two probabilities and we want to know the chance thatbothare true, we just multiply them together. So, we take the two Gaussian blobs and multiply them: What we’re left with is theoverlap, the region wherebothblobs are bright/likely. And it’s a lot more precise than eith...
If we have two probabilities and we want to know the chance thatbothare true, we just multiply them together. So, we take the two Gaussian blobs and multiply them: What we’re left with is theoverlap, the region wherebothblobs are bright/likely. And it’s a lot more precise than eith...
If we have two probabilities and we want to know the chance thatbothare true, we just multiply them together. So, we take the two Gaussian blobs and multiply them: What we’re left with is theoverlap, the region wherebothblobs are bright/likely. And it’s a lot more precise than eith...
Hmm. This looks like another Gaussian blob. As it turns out, when you multiply two Gaussian blobs with separate means and covariance matrices, you get anewGaussian blob with itsownmean and covariance matrix! Maybe you can see where this is going: There’s got to be a formula to get those...