dealing with acoustic noise part 1: spectral estimation

Dealing with Acoustic Noise

Part 1: Spectral Estimation

Mark Hasegawa-JohnsonUniversity of Illinois

Lectures at CLSP WS06July 20, 2006

Noise from the Perspective of the Brainstem

1. Something happened!! (VAD)

2. What happened?? (Recognition)

Noise from the Perspective of the Brainstem

1. Something happened!! (VAD)2. Which pixels belong to the new event

(auditory scene analysis)?3. What are their amplitudes (spectral

estimation)?4. What happened?? (Recognition)

A Speech Recognition Model (Mixture Gaussian)

• Problem: find the most probable class (Cn) given measurements of a function (f(S)) of the speech signal (S). For example, f(S) might be the PLP coefficients.

• Solution: Choose n such that p(Cn,S)>p(Cm,S) for n≠m.

• What is p(Cn,S)? The most effective current computational model is the mixture Gaussian: the weighted sum of exp(-|-f(S)|W2), where |x|W2≡xTWx. 2W is called the “precision” matrix.

How Should the Model React to Additive Noise?

• Suppose that we only have a noisy measurement, X:

... where V is independent noise. Then Cn should maximize:

Answer: By Computing fMMSE

Definition of fMMSE

Classical Estimators: Maximum Likelihood

Classical Estimators: Maximum A Posteriori

Classical Estimators: Minimum Mean Squared Error

Functions of Random Variables Since fMMSE(S)≠f(SMMSE), it is necessary to find the

probability density function of f(S) directly. Fortunately, the PDF of f(S) can always be computed from the PDF of S, as follows:

PDF of Speech: Time Domain• Speech samples s[n] are often

modeled as Gaussian, because Gaussian PDFs are easy to manipulate.

• In fact, noise tends to be Gaussian, but…

• Speech PDF is actually a mixture of Gaussian small-amplitude samples (the noise bits?) and Laplacian high-amplitude samples (the actual speech bits?)

PDF of Speech: Filter Outputs, e.g., STFT

• STFT is just a filter with complex-valued filter coefficients:

• Central Limit Theorem says Sk should be a 2D Gaussian, if the window is infinitely long…

Err…. Is it REALLY Gaussian?

If we ignore the previous slide, and pretend that Sk is a

complex Gaussian, then it is possible to analytically derive

the PDFs of |Sk|2, |Sk|, and phase of Sk. They are:

Classical Spectral Estimation: Assumptions

• One signal is ongoing (call it the “noise”), so its N is known (averaged over time prior to voice activity detection).

• The MMSE estimate of the other signal, S, combines two types of information:– a priori knowledge, S = E[|Sk|2]

• a priori SNR: k = S / N

–Maximum likelihood estimator, SML = |Xk|2-N

• Maximum likelihood SNR: k = |Xk|2 / N

Classical Spectral Estimation Results:Wiener Filter(Norbert Wiener, 1949)

Classical Spectral Estimation Results:MMSE Spectral Amplitude Estimate

(Ephraim and Malah, 1984)

Classical Spectral Estimation Results:MMSE Log Amplitude Estimate

(Ephraim and Malah, 1985)

How does it sound?

How does it sound?MVDR Beamformer

eliminates high-

frequency noise,

MMSE-logSA

eliminates low-

frequency noise

MMSE-logSA adds

reverberation at low

frequencies;

reverberation seems

to not effect speech

recognition accuracy

What about,oh, say…

PLP?

Loudness Spectrum• Perceptual LPC (PLP) begins by computing an

estimate of the perceptual loudness spectrum. – Step 1: filter the signal using complex-valued

Bark-scale critical-band filters hk[m]:

– Step 2: compress the amplitudes with a nonlinearity:

MMSE Estimate of the Perceptual Loudness Spectrum

• gPLP requires numerical integration (of u1/3e-u)• Numerical integration is a lot cheaper than it

used to be (e.g., via lookup table).

A Conservative Computational Auditory Model

Basilar Membrane:Mechanical

Filterbank(von Bekesy)

x(t)

VariableThresholdSynapses: Amplitude Compression(Ghitza, 1986)

AverageBackgroundLoudness lN

ChangeDetection

MMSEEstimator of “New Event”

E[ |S[k]|2/3 | X ]

Auditory Nerve Carries The Loudness

Spectrum~ |X[k]|2/3

(Fletcher;Hermansky)

Auditory Scene Analysis

Speech FeatureExtraction

PLP≈

PerceptualFormant

Extraction(Hermansky);

TandemFeatures

≈Perceptual

MagnetEffect

(Niyogi)

Change Detection (VAD)