處於『雜訊』以及『干擾』之世界中，即使打個『招呼』︰
都將與『統計』和『不確定性』為伍耶！？
因是在『【鼎革‧革鼎】…』篇章結束之前，特說『學後而識』的『重要性』，或連『機器』亦不可免乎？！
- K-Nearest Neighbor Classification (ipynb)
- Cross Validation (ipynb)
- Exercise: K-Nearest Neighbor Instrument Classification (ipynb)
- K-Means Clustering (ipynb)
- Exercise: Unsupervised Instrument Classification using K-Means (ipynb)
- Neural Networks (ipynb)
- Evaluation (ipynb)
- Genre Recognition (ipynb)
- Exercise: Genre Recognition (ipynb)
……
We can appreciate why we need additional intelligence in our systems — heuristics don’t go very far in the world of complex audio signals. We’ll be using scikit-learn’s implementation of the k-NN algorithm for our work here. It proves be a straightforward and easy-to-use implementation. The steps and skills of working with one classifier will scale nicely to working with other, more complex classifiers.
揣想是否能借『改寫』派生二之
A Python library for audio feature extraction, classification, segmentation and applications
This doc contains general info. Click [here] (https://github.com/tyiannak/pyAudioAnalysis/wiki) for the complete wiki
General
pyAudioAnalysis is a Python library covering a wide range of audio analysis tasks. Through pyAudioAnalysis you can:
- Extract audio features and representations (e.g. mfccs, spectrogram, chromagram)
- Classify unknown sounds
- Train, parameter tune and evaluate classifiers of audio segments
- Detect audio events and exclude silence periods from long recordings
- Perform supervised segmentation (joint segmentation – classification)
- Perform unsupervised segmentation (e.g. speaker diarization)
- Extract audio thumbnails
- Train and use audio regression models (example application: emotion recognition)
- Apply dimensionality reduction to visualize audio data and content similarities
『程式庫』至派生三的『經驗』，得到啟發也◎