My research journey showcases a diverse portfolio of projects with broad-ranging applications, including:

• Smart infrastructure and urban environments
• Analysis of everyday soundscapes
• Human–machine interaction and assistive technologies
• Acoustic monitoring in healthcare settings
• Intelligent noise monitoring

My involvement in mentoring students at various stages of their academic journey spans a wide range of topics and applications, including:

• Zero-shot learning
• Representation learning
• Active learning
• Deep learning for sound event detection
• Compensation for loudspeaker distortions
• Audio captioning
• Tire type classification
• Semi-supervised musical instrument recognition
• Guitar chord detection
• Parameter adaptation in nonlinear loudspeaker models

Deep Learning for Audio: Applying new approaches for audio content analysis.

• Introduction to computational audio content analysis
• Multi-label zero-shot audio classification, conference publication
• Positive and negative sampling strategies for self-supervised learning, conference publication
• Active learning for sound event detection, journal publication
• Sequential information in polyphonic sound event detection, conference publication

Sound Event Detection (SED): Designing robust models to detect and timestamp sound events in real-world, often noisy, environments. See a detailed introduction to sound event detection in everyday environments.

• Introduction to sound event detection in everyday environments
• Sound event detection with soft labels, conference publication
• Sound event detection: a tutorial, Magazine publication
• Sound event detection in the DCASE 2017 Challenge, journal publication
• Convolutional recurrent neural networks, journal publication

Acoustic Scene Classification (ASC): the process of training computers to identify different environments, such as streets, parks, or offices, based on their distinct acoustic characteristics.

• Data-efficient low-complexity ASC, conference publication
• Low-complexity ASC for multi-device audio, conference publication
• Audio-visual scene analysis, conference publication
• Generalization across devices, conference publication
• Closed and open set classification, conference publication

Evaluation Frameworks: Developing standardized metrics, protocols, and datasets for benchmarking audio analysis systems.

• Evaluation metrics for sound event detection
• Evaluation toolbox for Sound Event Detection
• Datasets and Evaluation, book chapter
• Metrics for polyphonic sound event detection, journal publication
• Joint measurement of localization and detection of sound events, conference publication
• Benchmarks for DCASE Challenge

Dataset Development: Creating annotated audio datasets and evaluation benchmarks for reproducible research, and the release of open datasets.

• Data acquisition: audio collection, annotation, and dataset creation
• Details about the key datasets used in my research
• Open datasets created and maintained by me
• Data-related tool
• DCASE DataList

Real-time Audio Analysis: Implementing real-time systems for on-device and streaming audio recognition.
See details about some related projects:

• Smart cities
• Assistive technologies
• Healthcare environments

Academic Impact: Over the past decade, my research has focused on neural networks, deep learning, and audio signal processing.

• I have authored more than 60 publications, which have collectively received over 8K citations with an h-index of 35.
• According to SciVal, I rank 8^th globally in scholarly output and 20^th in field-weighted citation impact within the topic area "Neural networks; Deep learning; Audio Signal Processing" (2014-2023).

Open Data and Community Resources:

• Co-authored and published over 20 open-access audio datasets while working at Tampere University. The datasets have been downloaded more than 200K times. The datasets are heavily utilized in machine learning competitions and academic studies as benchmarks for evaluating state-of-the-art systems.
• Maintaining DCASE DataList

Evaluation Standards and Tools: I have contributed to defining standard evaluation metrics for SED and sound event localization and detection (SELD), and helped establish evaluation setups for ASC:

• Metrics for polyphonic sound event detection, journal publication
• Joint measurement of localization and detection of sound events, conference publication

I also maintain several open-source toolboxes that are widely used in the community:

• sed_eval (83K pip downloads, 143 GitHub stars)
• dcase_util (128K pip downloads, 130 GitHub stars)
• sed_vis (120 GitHub stars)

Service to the Research Community:

• I actively contribute as a peer reviewer for leading journals and conferences (e.g., IEEE TASLP, IEEE JSTSP, ICASSP, WASPAA, EUSIPCO, DCASE, ISMIR, and AES).
• I have taken on several organizational roles within the DCASE Challenge, such as task coordinator and webmaster.
• I have also contributed to the DCASE Workshop in various capacities, including proceedings editor, technical chair, and publication chair.

Academic Illustrations in DCASE Research:

• Numerous illustrations for educational and scientific purposes, visually explaining complex concepts such as sound event detection, acoustic scene classification, and audio tagging.
• Illustrations are provided with Creative Commons license to enhance accessibility and understanding in the field, benefiting researchers, educators, and students.

Academic biography: See a concise overview of my career in academia, highlighting relevant qualifications, experience, and accomplishments.