Signal Processing and Machine Learning Projects

Integration of signal processing and machine learning is considered as an extensive application ranging from clinical diagnostics to financial prediction. Lot of time and knowledge is needed to complete your Signal Processing project successfully. We have a panel of ML expert writers. takes pride in assisting you for all research work. All research work on Signal Processing and Machine Learning Projects are undertaken by us here we combine both to derive exact result for the proposed problem.

 Here, we list out various research concepts by integrating these two domains:

  1. EEG Signal Categorization:
  • Goal: For several tasks such as epilepsy identification, emotion recognition or sleep stage categorization, we categorize Electroencephalogram (EEG) signals.
  • Process: Our approach preprocesses the EEG signals, retrieves important characteristics such as power spectral density. For the categorization process, we utilize ML methods like CNN, RNN, and SVM.
  1. Speech Recognition:
  • Goal: Our goal is to change spoken language into a text pattern.
  • Process: For categorization, we utilize deep learning frameworks such as transformers or LSTM and for feature extraction, consider Mel-Frequency Cepstral Coefficients (MFCC).
  1. Voice Command Recognition:
  • Goal: To begin actions, we recognize particular voice based commands.
  • Process: We carry out a recognition process using LSTM or CNN networks and consider MFCC or spectrograms as relevant characteristics.
  1. Image Denoising utilizing Autoencoders:
  • Goal: Our objective is to eliminate noises from images.
  • Process: Convolutional Autoencoders assist us to accomplish this process by feed the noisy image to the autoencoder and after the training process, we get the clean image.
  1. Radar Signal Processing for Object Identification:
  • Goal: By utilizing radar signals, we identify and categorize objects.
  • Process: Our research intends to preprocess the signals by utilizing doppler processing and other relevant methods, after that, for object categorization, we make use of machine learning frameworks.
  1. Environmental Sound Categorization:
  • Goal: We aim to categorize background sounds like traffic, rain and bird chirping effects.
  • Process: By employing Mel-scale spectrogram or Short Time Fourier Transform (STFT), we retrieve audio features and utilize CNN for the categorization process.
  1. Anomaly identification in Time Series Data:
  • Goal: In time series data like equipment telemetry and stock prices, we identify uncommon patterns.
  • Process: For the feature extraction process, our project utilizes Wavelet Transform or Fourier Transform and to identify anomalies, we employ Isolation forest or LSTM methods.
  1. Seismic Signal processing for Earthquake Identification:
  • Goal: Our work intends to identify seismic incidents or earthquakes.
  • Process: From seismic signals, we retrieve relevant features and utilize methods such as Gradient Boosting Machines or SVM for the identification process.
  1. ECG Heartbeat Categorization:
  • Goal: By analyzing Electrocardiogram (ECG) signals, we detect abnormal heartbeats.
  • Process: Our approach considers the MIT-BIH Arrhythmia database as an initial dataset and carries out various processes like heartbeat segmentation, retrieving important features such as interval and RR and categorization using CNN or Random Forest.
  1. Sound Source Localization:
  • Goal: In a particular circumstance, we discover the source of a sound.
  • Process: For the localization process, our research utilizes ML techniques, Time-Difference of Arrival (TDOA) and a set of microphones.
  1. Vibration Analysis for Equipment Maintenance:
  • Goal: By considering vibration factors, we forecast the maintenance demand for commercial equipment.
  • Process: Our project utilizes frequency or time domain based features and to forecast equipment damage or maintenance time-log, we employ machine learning techniques.
  1. Music Genre Categorization:
  • Goal: On the basis of genre, we categorize music data.
  • Process: From audio signals, we retrieve relevant characteristics such as spectral contrast, Tonnetz and chroma. Various methods such as CNN, SVM or Random Forest assist us to carry out the categorization process.


  • It is very important to carry out signal preprocessing procedures like filtering process, normalization, and feature extraction for every project. Because the feature’s standard influences the efficiency of our framework.
  • To simplify the preprocessing procedure, we utilize deep learning methods such as RNNs or CNNs that autonomously retrieve only essential features from the unprocessed signals.

We conclude that the integration of machine learning and signal processing enables us to retrieve important features from unprocessed data. A robust interpretation of both the fields (for instance: music and clinical signals) and the technical factors influence the great achievement of our project.

Our journal experts has numerous skills in machine learning like journal requirements and journal knowledge. So, they aid you to publish your paper in high reputed and international journal. Multiple revisions will be carried out to avoid mistake. Conference Paper writing is carried out by us as per your university rules and presented correctly.

Signal Processing and Machine Learning Project Ideas

Signal Processing and Machine Learning Projects Thesis Ideas

                   Add value to your research paper by our thesis services wonderful thesis ideas, thesis topics, thesis proposal and thesis writing are suggested and written by us. Thesis writing require experts and skills to complete the paper but here at our team has more than 18+ experience to finish of your paper. You can analyze the quality of our paper when you receive it and have supported more than 3000+ customers globally for Signal Processing and Machine Learning Projects.

Our work that we have done on Signal Processing and Machine Learning Project are listed below…. Stay in touch with us to score high grade.

  1. Carnatic Music Identification of Melakarta Ragas through Machine and Deep Learning using Audio Signal Processing


Audio signal processing, Raga Classification, Neural Networks, Machine Learning

            Using ML and DL our work concentrates on predicting raga, an Indian classical music. The method uses Librosa package for audio processing and feature extraction. Our study has different applications music education, therapy and synthesis. To predict raga of a given audio clip is difficult by using ML and DL methods to make it possible. Our study uses Librosa a python package for music analysis.

  1. A Survey on Machine Learning Techniques for Multimodal Biomedical Signal Processing


Biomedical signal processing, data fusion, deep learning, multimodal signal processing

            Our study offers an overview on ML and DL methods to multimodal signal processing for biomedical application. The preprocessing methods and the suggested algorithms for five significant biological applications are detection of cardiovascular disease, stress, cancer, covid19 and retinal disease. Processes for multimodal data type’s image and text are given for each situation.

  1. Graph Signal Processing Based Classification of Noisy and Clean PPG Signals Using Machine Learning Classifiers for Intelligent Health Monitor


Photoplethysmography, horizontal visibility graph, graph signal processing, signal quality assessment, convolution neural network, machine learning classifiers

            Photoplethysmography (PPG) signal quality is automatically assessed to eliminate unwanted signals and minimize false alarms caused by noise reading.  Our study offers a new PPG signal quality assessment (SQA) method by utilizing average feature extracted from Horizontal visibility Graph (HVG) and we also used six classifier methods namely SVM, CNN, MLP, RF, DT and NB. The NB based SQA achieves better accuracy.

  1. Fault Detection of Bearing using Signal Processing Technique and Machine Learning Approach


Spectrum analysis, DWT, J-48, bearing fault.

            Our work uses online ML technique and signal processing technique to detect the fault diagnosis for the obtained vibrating signals. Our work performs two parts. To examine the fault detection of bearing the first part involves conventional signal processing techniques like time domain analysis and spectrum analysis and the second part uses ML methods. Signals were processed using Matlab to derive Discrete Wavelet Features (DWT). Most important characters were extracted from J48. 

  1. Using Electroencephalographic Signal Processing and Machine Learning Binary Classification to diagnose Schizophrenia


Schizophrenia, EEG, Random Forest, Extra Trees, Support Vector Machine, K-Nearest Neighbor

            EEG signal can be used to detect brain functions in schizophrenic patients. Our work uses many methods to classify healthy and schizophrenic patients by post signal processing such as SVM, KNN, Random Forest and Extra Trees. Time series or frequency series EEG data were used to feature extraction in signal processing. Random forest gives the better performance.

  1. Bearing fault diagnosis using signal processing and machine learning techniques: A review


Bearing, Fault diagnosis.

            Bearing failure diagnosis system is in the increasing interest due to the availability of condition monitoring methods. Fault feature extraction is the function of signal processing techniques and machine learning methods that present an intelligent fault diagnosis system can find and detect the bearing faults. Using signal processing and ML our article discover certain fault diagnosis techniques.       

  1. Efficient and Direct Inference of Heart Rate Variability using Both Signal Processing and Machine Learning


 Heart Rate Variability

            Heart Rate Variability (HRV) measures the difference of time between sequential heartbeats and measures physical and mental health. Photoplethysmography also utilized to gather HRV. We first gather a large PPG signal dataset and HRV ground truth with this we enhance to combine ML and signal direct infer with HRV. Our model decision tree and multilevel perceptron shows low inference time.            

  1. Parkinson’s Disease Detection via Resting-State Electroencephalography Using Signal Processing and Machine Learning Techniques


Parkinson’s disease, Feature Extraction

             Electroencephalography (EEG) denotes the irregularities in Parkinson disease (PD) patients and the major task is the lack of consistent, accurate and systematic biomarker in PD to carefully watch them with medications and treatments. In our paper we have to gather EEG data and then preprocess every signal using some methods and extract relevant features using feature extraction methods. ML has been used to classify PD versus healthy control. 

  1. Detection of Oculomotor Dysmetria from Mobile Phone Video of the Horizontal Saccades Task Using Signal Processing and Machine Learning Approaches


Mobile phone video, horizontal saccades, eye movement analysis, entropy, functional connectivity, template matching, test-retest reliability.

            Our paper calculates the accessible method to detect and quantify oculomotor dysmetria. Data from iPhone videos of horizontal saccade test and a clinical challenge in ataxia were used to evaluate saccade irregularities using ML and signal processing. SVM can be utilized to test and train the ability of multiple signal processing and to differentiate individuals with and without oculomotor dysmetria. 

  1. A seismic signal processing framework using machine learning on an IoT devices for in the field pre-processing

Key Points:

Seismic signal processing, IoT in geophysics, framework

                                   Our work uses a group of noise data gathered from the field and the synthetic reflection seismogram produced from machine learning core. Internet of Things (IoT) may keep the trained core and preprocess the data in the field of QC, before delivering it to the office for further processing. This framework can simplify a complex signal with simultaneous noise reduction.

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