Seeking Bit-Perfect DSP Feature Extraction Tool in Python/ Notebook for Model Alignment

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Question/Issue:

I am experiencing numerical mismatch between the feature extraction pipeline implemented in the generated C++ SDK (specifically the MFE block, as seen in edge-impulse-run-dsp.h) and standard Python/TensorFlow libraries (e.g., tf.signal or librosa). This mismatch prevents me from accurately aligning my model training results (in Python) with the inference results (on the target device running C++).

I need a bit-perfect replication of the Edge Impulse C++ DSP pipeline to be run in a Python/Jupyter Notebook environment so I can train my model with features that exactly match those computed during on-device inference.

Does Edge Impulse provide a Python utility, module, or Jupyter Notebook template that guarantees the exact same numerical result as the deployed C++ DSP?

Project ID:

Context/Use case:

I am training a custom model using a Python framework (TensorFlow/Keras). The model requires MFE features extracted from audio data. To ensure successful deployment, the MFE feature vector used for training must match exactly the feature vector generated by the C++ runtime on the target device.

Steps Taken:

  1. Extracted MFE features using the generated C++ SDK on the target.
  2. Extracted MFE features using Python (tf.signal.stft + tf.signal.linear_to_mel_weight_matrix), ensuring all parameters (frame length, stride, Mel bins, sample rate) match the C++ configuration.
  3. Disabled Pre-emphasis in both C++ and Python to eliminate that variable.

Expected Outcome:

The floating-point feature vectors generated by the C++ code should be numerically identical (or nearly identical, considering minor float differences) to the feature vectors generated by the Python tool.

Actual Outcome:

The feature vectors show significant numerical discrepancies (due to library implementation differences, FFT result handling, or rounding logic, such as the $0\text{ Hz}$ to $300\text{ Hz}$ check in older C++ versions).

Reproducibility:

  • [x] Always

Environment:

  • Platform: [Ex: ESP32, Raspberry Pi, Custom board]
  • Build Environment Details: [Ex: Visual Studio Code]
  • OS Version: [Ex: Ubuntu 22.04]
  • Edge Impulse Version (Firmware):
  • **Edge Impulse CLI Version:

Logs/Attachments:

Additional Information:

My primary concern is model accuracy degradation due to this feature alignment issue. A bit-perfect Python DSP module would solve this training/inference alignment problem.