> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/predictive-ml/digital-signal-processing-dsp.md). # Digital Signal Processing (DSP) Once a signal has been decomposed into frequencies, the practical question is which helpers actually filter it, find its peaks, and detect beats. This page is a toolbox shelf: SciPy for time-domain signal processing and peak finding first, then beat detection with Librosa and a real-time detector, then two modeling notes on activity recognition and self-attention for sound. The same notes are in [Electronic Network Frequency Analysis](/ai-product/electronic-network-frequency-analysis.md) and [Feature Engineering](/predictive-ml/audio-feature-engineering.md). The base layer is SciPy. [Scipy signal processing](https://docs.scipy.org/doc/scipy/reference/signal.html) is the signal processing module (scipy.signal) in the SciPy v1.18.0 manual, and [Script find peaks](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks.html) is its find\_peaks reference, the function to reach for when the question is where a signal spikes. Peaks in audio lead directly to beat detection. [Real time bpm beat det](https://github.com/shunfu/python-beat-detector) is a Python beat detector that does real-time detection of beats for audio, calculates BPM, and flashes an LED strip in time with music. Librosa also covers time-domain processing and beat detection (and temp), but its original docs addresses no longer open and are kept at the end of the page. The same time-series thinking carries over to sensors. [Mastery on Human activity recognition, smartphones](https://machinelearningmastery.com/cnn-models-for-human-activity-recognition-time-series-classification/) is MachineLearningMastery.com on 1D convolutional neural network models for human activity recognition from smartphone data. (Out of place) — Christopher Dossman's [using self-attention for sound signal processing](https://medium.com/ai%C2%B3-theory-practice-business/toward-interpretable-music-tagging-with-self-attention-67a8136048d0) is Toward Interpretable Music Tagging with Self-Attention. Out place on a DSP shelf, it is a model rather than a signal helper. ## Deprecated links {% hint style="warning" %} These links and images no longer work. The original wording is kept here. A same-resource copy, when one was checked, is used above. {% endhint %} * Librosa. This address no longer opens: * Beat detection (and temp). This address no longer opens: --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/predictive-ml/digital-signal-processing-dsp.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.