# Tag Info

Accepted

### The difference between convolution and cross-correlation from a signal-analysis point of view

In signal processing, two problems are common: What is the output of this filter when its input is $x(t)$? The answer is given by $x(t)\ast h(t)$, where $h(t)$ is a signal called the "impulse ...

### The difference between convolution and cross-correlation from a signal-analysis point of view

The two terms convolution and cross-correlation are implemented in a very similar way in DSP. Which one you use depends on the application. If you are performing a linear, time-invariant filtering ...

### Scipy - Audio Processing

Personally I find Python one of the best choices out there and did myself some work in area of audio identification. You are welcomed to check for instance my software for automatic identification of ...

### Fast pitch recognition

"Is there a way to measure frequency (detect pitch) better than FFT, that is, with better resolution in less acquisition time?" yes there is. or are. there are multiple better ways to do musical ...

### Understanding the Difference Between MAP Estimation and ML Estimation

Maximium A Posteriori (MAP) and Maximum Likelihood (ML) are both approaches for making decisions from some observation or evidence. MAP takes into account the prior probability of the considered ...

### A Machine Learning Based Algorithm as an Alternative to the Matched Filter

Sure, you can learn the matched filter, as convolution with a filter is just a function applied to a signal, and e.g. Neural Networks (through the universal approximation theorem) are good function ...

### Isolate High Variance vs Low Variance Sections of Signals

What you did is the reasonable solution. From here you can do 2 things to mitigate your issues: Computation Efficiency You can use online calculation of the Mean and the STD. Remember when you move ...

### Expectation of deterministic signals

If $x(t)$ is a deterministic signal, then you have $E[x(t)]=x(t)$. Furthermore, if $Y(t)$ is a random process you have $E[x(t)Y(t)]=x(t)E[Y(t)]$.
Accepted

### How would you use machine learning for peak detection?

To be honest, I don't think CNNs, RNNs and LSTM are useful for this kind of problem – a bandpass filter followed by a threshold would be. Now, that would have three parameters: Lower cutoff ...
Accepted

### The power of signal processing when faced with hardware constraints

That's a very broad question, but I'll give it a shot. 1 . ... how much could signal processing actually do to help us here ? ... That's highly dependent on the specific problem. Sometimes a lot, ...