J0nasW/paperswithcode huggingface dataset:
Task: A-VB High
Area: speech

The A-VB High track, explores a high-dimensional emotion space for understanding vocal bursts. Participants will be challenged with predicting the intensity of 10 emotions (Awe, Excitement, Amusement, Awkwardness, Fear, Horror, Distress, Triumph, Sadness, and Surprise) associated with each vocal burst as a multi-output regression task. Participants will report the average Concordance Correlation Coefficient (CCC), as well as the Pearson correlation coefficient, across all 10 emotions. The baseline for this challenge will be based on CCC.
Task: Speaker Recognition
Area: speech

**Speaker Recognition** is the process of identifying or confirming the identity of a person given his speech segments.


<span class="description-source">Source: [Margin Matters: Towards More Discriminative Deep Neural Network Embeddings for Speaker Recognition ](https://arxiv.org/abs/1906.07317)</span>
Task: Cultural Vocal Bursts Intensity Prediction
Area: speech

to predict the intensity of 40 culture-specific emotions (10 emotions from each culture)
Task: Speech Separation
Area: speech

The task of extracting all overlapping speech sources in a given mixed speech signal refers to the **Speech Separation**. Speech Separation is a special scenario of source separation problem, where the focus is only on the overlapping speech signal sources and other interferences such as music or noise signals are not the main concern of the study.


<span class="description-source">Source: [A Unified Framework for Speech Separation ](https://arxiv.org/abs/1912.07814)</span>

Image credit: [Speech Separation of A Target Speaker Based on Deep Neural Networks](http://staff.ustc.edu.cn/~jundu/Publications/publications/ICSP2014_Du.pdf)
Task: Text-To-Speech Synthesis
Area: speech

**Text-To-Speech Synthesis** is a machine learning task that involves converting written text into spoken words. The goal is to generate synthetic speech that sounds natural and resembles human speech as closely as possible.
Task: Speech-to-Speech Translation
Area: speech

Speech-to-speech translation (S2ST) consists on translating speech from one language to speech in another language. This can be done with a cascade of automatic speech recognition (ASR), text-to-text machine translation (MT), and text-to-speech (TTS) synthesis
sub-systems, which is text-centric. Recently, works on S2ST without relying on intermediate text representation is emerging.
Task: Automatic Speech Recognition (ASR)
Area: speech

**Automatic Speech Recognition (ASR)** involves converting spoken language into written text. It is designed to transcribe spoken words into text in real-time, allowing people to communicate with computers, mobile devices, and other technology using their voice. The goal of Automatic Speech Recognition is to accurately transcribe speech, taking into account variations in accent, pronunciation, and speaking style, as well as background noise and other factors that can affect speech quality.
Task: A-VB Two
Area: speech

In the A-VB Two track, we investigate a low-dimensional emotion space that is based on the circumplex model of affect. Participants will predict values of arousal and valence (on a scale from 1=unpleasant/subdued, 5=neutral, 9=pleasant/stimulated) as a regression task. Participants will report the average Concordance Correlation Coefficient (CCC), as well as the Pearson correlation coefficient, across the two dimensions. The baseline for this challenge will be based on CCC.
Task: Utterance-level pronounciation scoring
Area: speech

Total pronunciation score of an utterance.
Task: Spoken language identification
Area: speech

Identify the language being spoken from an audio input only.
Task: Speaker Diarization
Area: speech

**Speaker Diarization** is the task of segmenting and co-indexing audio recordings by speaker. The way the task is commonly defined, the goal is not to identify known speakers, but to co-index segments that are attributed to the same speaker; in other words, diarization implies finding speaker boundaries and grouping segments that belong to the same speaker, and, as a by-product, determining the number of distinct speakers. In combination with speech recognition, diarization enables speaker-attributed speech-to-text transcription.


<span class="description-source">Source: [Improving Diarization Robustness using Diversification, Randomization and the DOVER Algorithm ](https://arxiv.org/abs/1910.11691)</span>
Task: Speech Enhancement
Area: speech

**Speech Enhancement** is a signal processing task that involves improving the quality of speech signals captured under noisy or degraded conditions. The goal of speech enhancement is to make speech signals clearer, more intelligible, and more pleasant to listen to, which can be used for various applications such as voice recognition, teleconferencing, and hearing aids.

<span style="color:grey; opacity: 0.6">( Image credit: [A Fully Convolutional Neural Network For Speech Enhancement](https://arxiv.org/pdf/1609.07132v1.pdf) )</span>
Task: Voice Cloning
Area: speech

Voice cloning is a highly desired feature for personalized speech interfaces. Neural voice cloning system learns to synthesize a person’s voice from only a few audio samples.
Task: Speech Emotion Recognition
Area: speech

**Speech Emotion Recognition** is a task of speech processing and computational paralinguistics that aims to recognize and categorize the emotions expressed in spoken language. The goal is to determine the emotional state of a speaker, such as happiness, anger, sadness, or frustration, from their speech patterns, such as prosody, pitch, and rhythm.

For multimodal emotion recognition, please upload your result to [Multimodal Emotion Recognition on IEMOCAP](https://paperswithcode.com/sota/multimodal-emotion-recognition-on-iemocap)
Task: Speaker Verification
Area: speech

Speaker verification is the verifying the identity of a person from characteristics of the voice.

<span style="color:grey; opacity: 0.6">( Image credit: [Contrastive-Predictive-Coding-PyTorch
](https://github.com/jefflai108/Contrastive-Predictive-Coding-PyTorch) )</span>
Task: Speech Synthesis
Area: speech

Speech synthesis is the task of generating speech from some other modality like text, lip movements etc. 

Please note that the leaderboards here are not really comparable between studies - as they use mean opinion score as a metric and collect different samples from Amazon Mechnical Turk.

<span style="color:grey; opacity: 0.6">( Image credit: [WaveNet: A generative model for raw audio](https://deepmind.com/blog/article/wavenet-generative-model-raw-audio) )</span>
Task: Speech Denoising
Area: speech

Obtain the clean speech of the target speaker by suppressing the background noise.
Task: Speech Dereverberation
Area: speech

Removing reverberation from audio signals
Task: Vocal Bursts Type Prediction
Area: speech

predict the type of given vocal bursts
Task: Multimodal Emotion Recognition
Area: speech

This is a leaderboard for multimodal emotion recognition on the IEMOCAP dataset. The modality abbreviations are
A: Acoustic
T: Text
V: Visual

Please include the modality in the bracket after the model name.

All models must use standard five emotion categories and are evaluated in standard leave-one-session-out (LOSO). See the papers for references.
Task: Audio-Visual Speech Recognition
Area: speech

Audio-visual speech recognition is the task of transcribing a paired audio and visual stream into text.
Task: Traffic Prediction
Area: time-series

**Traffic Prediction** is a task that involves forecasting traffic conditions, such as the volume of vehicles and travel time, in a specific area or along a particular road. This task is important for optimizing transportation systems and reducing traffic congestion.

<span style="color:grey; opacity: 0.6">( Image credit: [BaiduTraffic](https://github.com/JingqingZ/BaiduTraffic) )</span>
Task: Predict clinical outcome
Area: time-series

A cost-based metric that considers the costs of algorithmic prescreening, expert screening, treatment, and diagnostic errors that result in late or missed treatments. This metric is further described here: https://moody-challenge.physionet.org/2022/
Task: W-R-N Sleep Staging
Area: time-series

3-class Sleep Staging into 
- Wake 
- Rem
- NREM
Task: W-R-L-D Sleep Staging
Area: time-series

4-class Sleep Staging into 
- Wake
- REM
- LIGHT SLEEP
- DEEP SLEEP
Task: Automatic Modulation Recognition
Area: time-series

Automatic modulation recognition/classification identifies the modulation pattern of communication signals received from wireless or wired networks.
Task: LWR Classification
Area: time-series

T4: LWR Classification: Predicting if the subject is Listening, Writing, or Resting from physiological signals (EEG, GSR, PPG). Labels: 0-listening, 1-writing, 2-resting. Classification tasks.
Task: energy management
Area: time-series

energy management is to schedule energy units inside the systems, enabling an reliable, safe and cost-effective operation
Task: New Product Sales Forecasting
Area: time-series

Sales forecasting of new product, which the market hasn’t seen
before.
Task: Change Point Detection
Area: time-series

**Change Point Detection** is concerned with the accurate detection of abrupt and significant changes in the behavior of a time series.

Change point detection is the task of finding changes in the underlying model of a signal or time series. They are two main methods: 

1) Online methods, that aim to detect changes as soon as they occur in a real-time setting

2) Offline methods that retrospectively detect changes when all samples are received.

Source: [Selective review of offline change point detection methods](https://arxiv.org/pdf/1801.00718.pdf)
Task: Time Series Forecasting
Area: time-series

**Time Series Forecasting** is the task of fitting a model to historical, time-stamped data in order to predict future values. Traditional approaches include moving average, exponential smoothing, and ARIMA, though models as various as RNNs, Transformers, or XGBoost can also be applied. The most popular benchmark is the ETTh1 dataset. Models are typically evaluated using the Mean Square Error (MSE) or Root Mean Square Error (RMSE). 

<span style="color:grey; opacity: 0.6">( Image credit: [ThaiBinh Nguyen](https://github.com/tn16jv/Stock-Price-Prediction) )</span>
Task: Exponential degradation
Area: time-series

Exponential degradation  used to solve problems where systems exposed to an exponential loss in performances such as reparable industrial systems.
Task: Video Quality Assessment
Area: time-series

Video Quality Assessment is a computer vision task aiming to mimic video-based human subjective perception. The goal is to produce a mos score, where higher score indicates better perceptual quality. Some well-known benchmarks for this task are KoNViD-1k, LIVE-VQC, YouTube-UGC and LSVQ.  SROCC/PLCC/RMSE are usually used to evaluate the performance of different models.
Task: Social Media Popularity Prediction
Area: time-series

Social Media Popularity Prediction (SMPP) aims to predict the future popularity (e.g., clicks, views, likes, etc.) of online posts automatically via plenty of social media data from public platforms. It is a crucial problem for social media learning and forecasting and one of the most challenging problems in the field. With the ever-changing user interests and public attention on social media platforms, how to predict popularity accurately becomes more challenging than before. This task is valuable to content providers, marketers, or consumers in a range of real-world applications, including multimedia advertising, recommendation system, or trend analysis.
Task: EEG based sleep staging
Area: time-series

Sleep staging from only EEG signal
Task: Electroencephalogram (EEG)
Area: time-series

**Electroencephalogram (EEG)** is a method of recording brain activity using electrophysiological indexes. When the brain is active, a large number of postsynaptic potentials generated synchronously by neurons are formed after summation. It records the changes of electric waves during brain activity and is the overall reflection of the electrophysiological activities of brain nerve cells on the surface of cerebral cortex or scalp. Brain waves originate from the postsynaptic potential of the apical dendrites of pyramidal cells. The formation of synchronous rhythm of EEG is also related to the activity of nonspecific projection system of cortex and thalamus. EEG is the basic theoretical research of brain science. EEG monitoring is widely used in its clinical application.
Task: Time Series Classification
Area: time-series

**Time Series Classification** is a general task that can be useful across many subject-matter domains and applications. The overall goal is to identify a time series as coming from one of possibly many sources or predefined groups, using labeled training data. That is, in this setting we conduct supervised learning, where the different time series sources are considered known.


<span class="description-source">Source: [Nonlinear Time Series Classification Using Bispectrum-based Deep Convolutional Neural Networks ](https://arxiv.org/abs/2003.02353)</span>
Task: ECG based Sleep Staging
Area: time-series

Sleep Staging from only ECG signal
Task: Time Series Clustering
Area: time-series

**Time Series Clustering** is an unsupervised data mining technique for organizing data points into groups based on their similarity. The objective is to maximize data similarity within clusters and minimize it across clusters. Time-series clustering is often used as a subroutine of other more complex algorithms and is employed as a standard tool in data science for anomaly detection, character recognition, pattern discovery, visualization of time series.


<span class="description-source">Source: [Comprehensive Process Drift Detection with Visual Analytics ](https://arxiv.org/abs/1907.06386)</span>
Task: Earth Surface Forecasting
Area: time-series

Conditional forecasting of future multi-spectral imagery.
Task: Trajectory Modeling
Area: time-series

The equivalent of language modeling but for trajectories.
Task: Seismic source localization
Area: time-series

Locating a seismic source using seismometer recordings
Task: Semanticity prediction
Area: time-series

T3: Semanticity Prediction: Estimating the semanticity perceived by Listener from physiological signals (EEG, GSP, PPG). Label: 0-(semantic), 1-(non-semantic). Binary classification problem.
Task: PAIR TRADING
Area: time-series

Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets.
Task: Solar Flare Prediction
Area: time-series

Solar flare prediction in heliophysics
Task: Lip password classification
Area: time-series

A classification task that predicts whether the designated user is uttering the designated password.
Task: Algorithmic Trading
Area: time-series

An algorithmic trading system is a software that is used for trading in the stock market.
Task: Multimodal Association
Area: time-series

**Multimodal association** refers to the process of associating multiple modalities or types of data in time series analysis. In time series analysis, multiple modalities or types of data can be collected, such as sensor data, images, audio, and text. Multimodal association aims to integrate these different types of data to improve the understanding and prediction of the time series.

For example, in a smart home application, sensor data from temperature, humidity, and motion sensors can be combined with images from cameras to monitor the activities of residents. By analyzing the multimodal data together, the system can detect anomalies or patterns that may not be visible in individual modalities alone.

Multimodal association can be achieved using various techniques, including deep learning models, statistical models, and graph-based models. These models can be trained on the multimodal data to learn the associations and dependencies between the different types of data.
Task: Intelligent Communication
Area: time-series

Intelligently decide (i) the content of data
to be shared/communicated and (ii) the direction in which the chosen
data is transmitted.
Task: Fire Detection
Area: time-series

Detection of fire using multi-variate time series sensor data.
Task: Time Series Regression
Area: time-series

Predicting one or more scalars for an entire time series example.
Task: Imputation
Area: time-series

Substituting missing data with values according to some criteria.
Task: Time Series Analysis
Area: time-series

**Time Series Analysis** is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

<span style="color:grey; opacity: 0.6">( Image credit: [Autoregressive CNNs for Asynchronous Time Series](https://arxiv.org/pdf/1703.04122v4.pdf) )</span>
Task: Remaining Useful Lifetime Estimation
Area: time-series

Estimating the number of machine operation cycles until breakdown from the time series of previous cycles.
Task: Eeg Decoding
Area: time-series

**EEG Decoding** - extracting useful information directly from EEG data.
Task: Predictive Process Monitoring
Area: time-series

A branch of predictive analysis that attempts to predict some future state of a business process.
Task: Episode Classification
Area: time-series

The episode classification is a branch of the classification aiming to classify groups of observations of a Time Series. (Example: critical episodes/ normal episode)
Task: Classify murmurs
Area: time-series

Classify murmurs based on Phonocardiograms (PCGs)
Task: Time Series Prediction
Area: time-series

The goal of **Time Series Prediction** is to infer the future values of a time series from the past.


<span class="description-source">Source: [Orthogonal Echo State Networks and stochastic evaluations of likelihoods ](https://arxiv.org/abs/1601.05911)</span>
Task: Stock Price Prediction
Area: time-series

**Stock Price Prediction** is the task of forecasting future stock prices based on historical data and various market indicators. It involves using statistical models and machine learning algorithms to analyze financial data and make predictions about the future performance of a stock. The goal of stock price prediction is to help investors make informed investment decisions by providing a forecast of future stock prices.
Task: Human motion prediction
Area: time-series

Action prediction is a pre-fact video understanding task, which focuses on future states, in other words, it needs to reason about future states or infer action labels before the end of action execution.
Task: Attention Score Prediction
Area: time-series

Auditory Attention Score Prediction: Estimating the attention level of Listener from physiological signals (EEG, GSR, PPG), a regression task. The attention score ranges from 0 to 100.
Task: Noise Level Prediction
Area: time-series

T2: Noise Level Prediction: Estimating the noise level experienced by the Listener from physiological signals (EEG, GSR, PPG). Six different levels of  background noise (SNR)
Label: -6, -3, 0, 3, 6, and inf (noise-free)  in dB.
Task: Sequential Bayesian Inference
Area: time-series

Also known as Bayesian filtering or [recursive Bayesian estimation](https://en.wikipedia.org/wiki/Recursive_Bayesian_estimation), this task aims to perform inference on latent state-space models.
Task: Phonocardiogram Classification
Area: time-series

Classify labels/murmur/clinical outcome based on Phonocardiograms (PCGs)