Performance Calibration: Bird sound classifier - Impulses

Name	Input	DSP Blocks	Learn Blocks	f32_V_Acc	f32_T_Acc	i8_V_Acc	i8_T_Acc	f32_Latency	f32_Flash	i8_Latency	i8_Flash
Time series data, Audio (MFCC), Neural Network (Keras) #2 (1D convolutional network (best performance))	2,000ms.	MFCC	NN Classifier	91.5%	82.6%	91.8%	-	3 ms.	41.0K	3 ms.	36.4K	View impulse
Time series data, Audio (MFCC), Neural Network (Keras) #1 (2D convolutional network)	2,000ms.	MFCC	NN Classifier	87.1%	-	86.9%	-	7 ms.	51.0K	7 ms.	34.2K	View impulse
Time series data, Audio (MFCC), Neural Network (Keras) #3 (1D convolutional network with augmentation)	2,000ms.	MFCC	NN Classifier	90.7%	-	90.7%	-	3 ms.	41.0K	3 ms.	36.4K	View impulse

Impulse information

Tags - Impulse tags

Source - Impulse source (Manual vs EON Tuner)

Key metrics

f32_V_Acc - Accuracy (float32, validation set)

f32_T_Acc - Accuracy (float32, test set)

i8_V_Acc - Accuracy (int8, validation set)

i8_T_Acc - Accuracy (int8, test set)

f32_Latency - float32 Latency for the complete impulse.

f32_Flash - float32 Flash for the complete impulse.

i8_Latency - int8 Latency for the complete impulse.

i8_Flash - int8 Flash for the complete impulse.

Input block config

Win_Size - Window size

Win_Incr - Window increase

Freq - Frequency

Processing block config

Noc - Mel Frequency Cepstral Coefficients > Number of coefficients (Processing Block #1)

Fra_len - Mel Frequency Cepstral Coefficients > Frame length (Processing Block #1)

Fra_str - Mel Frequency Cepstral Coefficients > Frame stride (Processing Block #1)

Fil_num - Mel Frequency Cepstral Coefficients > Filter number (Processing Block #1)

FFT_len - Mel Frequency Cepstral Coefficients > FFT length (Processing Block #1)

Nws - Mel Frequency Cepstral Coefficients > Normalization window size (Processing Block #1)

Low_fre - Mel Frequency Cepstral Coefficients > Low frequency (Processing Block #1)

Coe - Pre-emphasis > Coefficient (Processing Block #1)

Shi - Pre-emphasis > Shift (Processing Block #1)

Learn block config

Model - ML Model architecture (Learn Block #1)

Tc - Training cycles (Learn Block #1)

Lr - Learning rate (Learn Block #1)

Lo - Use learned optimizer? (Learn Block #1)

Acw - Auto-weight classes (Learn Block #1)

Data_Aug - Data augmentation (Learn Block #1)

Val_Size - Validation set size (Learn Block #1)

Mode - Visual / expert mode (Learn Block #1)

Accuracy metrics

No columns

Target device

Define your target device requirements to inform model optimizations and performance calculations. No device yet? Use the default settings which you can change at any time.

Target device

Processor family

Clock rate

| MHz

Max

Custom device name

Application budget

Specify the available RAM and ROM for the model's operation, along with the maximum allowed latency for your specific application. Not sure yet? Start with the defaults and modify them later on.

RAM

| KB

Max

ROM

| KB

Max

Latency

| ms

Max

Experiments

Time series data, Audio (MFCC), Neural Network (Keras) #2 (1D convolutional network (best performance))

Time series data, Audio (MFCC), Neural Network (Keras) #1 (2D convolutional network)

Time series data, Audio (MFCC), Neural Network (Keras) #3 (1D convolutional network with augmentation)