# Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers
## 1 Introduction  
Architectural acoustics is the science of studying acoustic environments in architecture. In the past, subjects of architectural acoustic research were usually concert halls, opera houses, and theaters. However, good indoor acoustic environments are not limited to professional performance spaces. In recent years, architectural acoustics in non-musical professional use spaces has begun to receive attention, in spaces such as offices, libraries, multi-purpose spaces, etc. [1; 2; 3; 4; 5]. The concept of multi-functional space has grown more and more popular, especially in schools. Combining the auditorium and indoor physical activity space is a well-established design method that can maximize the use of school space and budget. However, each activity in a multi-functional space has its own requirements, and acoustic design can be difficult for this kind of space [6]. For ordinary spaces, the importance of acoustics is relatively low, but for such large-scale performance spaces as concert halls and theaters, the importance of acoustics is considerable, and simulation software such as Odeon, Ease, etc. is usually adopted for simulation and design. Compared with the above two venues, the small and medium-sized multi-functional activity centers referred to in this research have certain requirements for acoustics, but they are not as strict as those of a special performance space. In practice, such spaces do not use software for simulation. However, if simulation software cannot be introduced in the design stage, then knowing the various acoustic indicators, such as clarity (C50, C80), speech transmission index (STI), etc., is difficult. In Taiwan, small and medium-sized activity centers like school auditoriums and gym-nasiums are common multi-functional spaces that are often used for performances, singing, and speeches. Different usage scenarios should be matched with different architectural acoustic design standards. However, most architectural cases are designed using only traditional estimation formulas for confirming the reverberation time [7; 8; 9]. Although the estimation formula is quick and simple, many details are ignored in the calculation process. [PERSON] [10] mentioned that the reverberation time obtained by the Sabin or Eyring formula depends only on the volume of the room and the total absorption area. These formulas assume that the probability of a wall being hit by a sound ray is proportional to its size and is not related to the previous history of the ray. In addition, the shape of the space and the placement of sound-absorbing materials also affect the reverberation time. For rooms with non-uniform distribution of sound absorption (especially rectangular rooms), the Sabine and Eyring formulas tend to underestimate the real RT [11]. A study by [PERSON] [12] stated that when calculating the RT in a concert hall, if heavily upholstered or non-rectangular space was used, the Sabine formula needs to be corrected by adding the room volume. Machine learning (ML) is a branch of artificial intelligence (AI) primarily focused on making computers learn automatically, finding rules or patterns by analyzing large amounts of data, and making predictions on unknown data. ML can be roughly divided into four categories based on the learning method used: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning [13; 14]. ML has been widely used in many fields, including disease diagnosis [15], stock trend prediction [16], image and speech recognition [17; 18], information extraction [19], etc. In the ML approach, a prediction model can be trained with input data to achieve a goal without solving theoretic equations. For architectural acoustics, [PERSON] & [PERSON] [20] used 71 spaces, including concert halls, auditoriums, and cultural centers, to predict the reverberation time using neural networks.