Noise Estimation

In the chapter "Insights into Automotive Noise PSD Estimation Based on Multiplicative Constants", authored by Vasudev Kandade Rajan and Gerhard Schmidt, of the book entitled "Intelligent Vehicles and Transportation - Vehicles, Drivers, and Safety" a noise estimation scheme was presented. Here, you can find example code that you are allowed to use for non-commercial applications.

Audio examples

The noise recording was made in a car driving at about 100 km/h. The speech example (female, German) was taken from the (free) SPID corpus. Details can be found here.

Code listings

Please have a look on the code listings. If you want to use it, we recommend to download this zip file. It contains also the required examples (audio files) and the folder structure that is assumed in order to run the Matlab code correctly.

{slider title="Main programm (signal mixing and result analysis)" class="grey" open="false"}

{easycodeinc https://dss.tf.uni-kiel.de/images/research/publications/additional_material/noise_estimation/main.m, 0}

{slider title="Noise estimation (the core of this project)" class="grey" open="false"}

{easycodeinc https://dss.tf.uni-kiel.de/images/research/publications/additional_material/noise_estimation/noise_estimation.m, 0}

{/sliders}

We are delighted to announce that Marten Finck has been awarded the GaS prize for his Master's thesis on "Evaluation of Speech Quality Using Machine Learning".

In his work, he investigated possible side effects of magnetic resonance-guided focussed ultrasound treatment (MRgFUS) on speech quality in patients with neurological diseases such as Parkinson's disease and essential tremor. This innovative and non-invasive treatment method is used to reduce tremor, i.e. the shaking of limbs.

To evaluate the effects on speech, pre- and post-operative speech recordings of 56 patients were analysed: Speech quality was assessed in five categories (monotony, clarity, fluency, auditory effort and vocal tremor) through a survey with 21 participants and used to create a convolutional neural network (CNN). This generates an objective assessment of speech quality using mel frequency cepstral coefficients (MFCCs), fundamental frequency and loudness. A mean absolute error (MAE) of 0.81 on a scale of 0 to 10 was achieved, compared to a standard deviation of 0.67 for the survey participants compared to a reference assessment. The evaluation of the survey revealed minor positive and negative changes in speech quality, which were balanced overall and tended to indicate natural speech variations in connection with the patients' underlying illnesses.

Congratulations on this well-deserved honour!