# Confidence Learning for Semi-Supervised Acoustic Event Detection
## 1 Introduction  
Acoustic event detection (AED) is a task for identifying the categories and timestamps of target sound events in continuous audio recordings. As one of the core technologies in non-verbal sound perception and understanding, AED is widely deployed in various applications, such as noise monitoring for smart cities [1], noturmally migrating bird detection [2], surveillance systems [3], and multimedia indexing [4]. Traditional approaches of AED mainly draw on the ideas of speech and the music signal process. Classic features such as Mel frequency cepstral coefficients (MFCCs) are sent into machine learning classifiers, such as the Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs) [5, 6]. Recently, deep neural networks (DNNs) [7, 8], especially convolutional neural networks (CNNs) [4, 9] and convolutional recurrent neural networks (CRNNs) [10], have been widely applied to AED tasks and achieve excellent performance. However, these methods are dependent on a large amount of strongly labeled training data with both types and timestamps. With the increase of acoustic event categories, high-quality manual annotations become hard to afford in terms of time and economy in practical applications. In comparison, synthetic strongly labeled data, weakly labeled data with only clip-level categories, and unlabeled data are widely available. Therefore, research and competitions [11, 12] are turned to semi-supervised acoustic event detection (SAED) with the above data. SAED inherits the paradigm of semi-supervised learning to use unlabeled data. That is, pseudo-labels are generated for unlabeled data as the training target. Therefore, the quality of pseudo-labels plays an important role. There are at least two approaches to improve thepseudo-labels quality in the SAED area. One is to improve the model that generates pseudo-labels. As high-level features with a larger compression scale are more suitable for clip-level classification, [PERSON] [13] and [PERSON] [14] introduce an extra branch or model with larger sampling sizes to produce high-level features with broader vision and better clip-level pseudo-labels for unlabeled data. Another way is to design a better pseudo-labels generation policy. Two effective semi-supervised learning methods are led into SAED: mean teacher and self-training. Mean teacher [15] averages model weights over steps to form a target-generating teacher model. A modified mean teacher model benefits SAED by employing both frame-level and clip-level consistency loss [14; 16; 17; 18]. Self-training [19], a simple but effective bootstrapping semi-supervised method, cycles retraining the model with part of its predictions as pseudo-labels. Self-training methods adopted in SAED [20; 21; 22] retrained only once and employed a small part of unlabeled data with high probabilities. These approaches filtered unlabeled data by using the posterior distribution to ensure the quality of the pseudo-labels. These two approaches have shown their respective strength, and their combination may bring further improvement. Nevertheless, the effect of the combination is out of the scope of this article. This paper focuses on introducing a better pseudo-labels generation and usage method. We notice that mean teacher and self-training yield impressive performance, but there are three problems with self-training. First, the probability is not a calibrated indicator for evaluating the correctness of the model predictions. As modern neural network classifiers are designed to produce output probabilities prone to extreme values, incorrect predictions can be generated with high probabilities [23]. Second, simplified self-training methods [20; 21; 22] lose a considerable amount of information. Third, these methods ignore true negative predictions. Nevertheless, data for SAED are extremely imbalanced. Massive correct negative predictions are beneficial to the retraining process.