Estimating Room Impulse Responses with Multiple Sound Sources

In many real-life situations, we encounter rooms with multiple sounds coming from different places. Each of these sounds makes its own path to the listener's ears, making it hard to know exactly how the room affects what we hear. This is known as the Room Impulse Response (RIR). Each sound source creates a different RIR based on where it is located and how it is facing.

When there are many sounds at once, figuring out which RIR to focus on can be tricky. Each sound source has its own unique response, and we need to find a way to estimate a single, representative RIR that describes the overall Acoustics of the room. This is important for various applications, like virtual reality, improving speech recognition, and more.

It’s tough to get accurate RIRs just by measuring them in physical spaces. This is why it has become essential to develop methods to estimate them. One approach is blind estimation, which means predicting RIRs based on recordings of sounds that have already bounced around the room. Yet, many existing methods only look at one sound source at a time, not considering the reality that multiple sources often fill our spaces.

The challenge lies in how different RIRs can change depending on where the source and listener are positioned. When thinking about how to estimate an RIR in environments with multiple Sound Sources, you must decide whether to focus on just one source's response or to find a single response that captures the general acoustic characteristics of the room.

Our research introduces the idea of estimating a central RIR in environments where there are multiple sources. The premise is that one representative RIR can effectively describe what the room sounds like. People can often tell that sounds are coming from the same room, even if the sources are in different spots.

It’s also known that while the early sounds and direct sounds from the RIR are affected by how far the sources are from the listener, the later sounds provide a broader view of the room’s sound characteristics. Therefore, there are shared features among RIRs from the same room.

This study details a way to train models that can estimate these representative RIRs effectively, including how to process the data and evaluate performance in such environments. Additionally, we discuss how to change the estimated RIRs into Binaural filters, which are useful for creating realistic spatial audio experiences.

#Related Works

There has been significant work in the area of blind estimation of RIRs. Many of these studies have focused on single sound source environments, using various techniques rooted in signal processing and deep learning. As research has progressed, the ability to estimate RIRs directly in the time domain has improved. However, the lack of sufficient data for training models continues to be a challenge, even with the creation of synthetic data.

In environments with multiple sound sources, the need for RIRs collected from many locations in various rooms adds to the complexity. Another line of research involves using images, videos, or 3D models to estimate RIRs, providing another layer of analysis for sound rendering in rooms. In some cases, RIRs are synthesized based on the locations of sound sources and the room’s layout, which can be particularly beneficial for virtual reality settings.

In our work, we define the representative RIR based on a source located about 1.5 meters from the listener. This distance serves as a standard conversational distance for people, making it relevant for applications like augmented reality or virtual audio scenarios. Our model is designed to predict this representative RIR whenever it receives recordings of sounds reflecting off multiple sources.

#Dataset Preparation

To create a model suitable for predicting RIRs in environments with multiple sources, we needed a dataset of RIRs measured in various rooms, ideally several hundred unique spaces. While we had access to a limited number of real-world RIRs, we complemented this with a large synthetic dataset, which includes thousands of rooms and numerous RIRs per room.

To generate this synthetic dataset, we used existing room simulation methods that allowed us to create RIRs under various conditions, focusing on realistic sound behaviors. We improved the generated RIRs by adjusting the late Reverberation aspects, ensuring they sounded more natural and consistent with real-world expectations.

After compiling our datasets, we observed that while there are variations in RIRs within a room, we can also see some trends and similarities. For example, certain values related to reverberation time can show only slight variations, while other values can differ significantly based on how sounds bounce around.

Before feeding this data into our model, we performed a normalization process. This involved identifying a representative RIR for each room and adjusting the amplitude levels across RIRs, ensuring that each was comparable while maintaining essential acoustic characteristics.

#Model and Training Method

To apply our training method, we developed a model that can generate RIRs directly from the reverberated signals. This model handles early reflections and late reverberation separately, allowing it to create more accurate RIRs.

In training, we used combinations of different sources to create an input signal. By blending one to six different reverberated sources, we simulated real-life scenarios to better prepare the model to predict the RIR. The model was trained using a multi-resolution approach, which allows it to assess and improve its RIR predictions over time.

We aimed for the model to show consistent performance no matter how many sources were involved. Evaluating the model’s success was based on metrics that compare predicted RIRs with known ground-truth values across different datasets.

#Post-Processing for Binaural Rendering

Our training model is enhanced by post-processing, which helps convert monoaural RIRs into binaural representations. This is crucial for applications in spatial audio where sounds need to appear to come from specific locations.

By updating just the late reverberation part of the predicted RIR while keeping earlier sounds intact, we can create sound scenarios that feel more realistic. This approach has been confirmed through tests, showing that listeners can differentiate between various rooms based on the late reverberation they detect.

Our final output combines synthesized late components with established head-related impulse response filters to meet specific audio requirements. By rescaling and mixing these elements, we create a robust audio representation suitable for our targeted applications.

#Results and Discussion

We conducted various evaluations to measure the effectiveness of our training method, comparing it to existing approaches. This included both objective metrics, such as reverberation time and clarity of sound, and qualitative listener tests that helped gauge how people perceive the difference between real and estimated RIRs.

Our results indicated that our model performs consistently across different room configurations, even as the number of sound sources increases. This stability was particularly evident in environments with more acoustic complexity, showcasing the model's capabilities.

Listener tests affirmed that the predicted RIRs from our method were perceived as closer to the actual RIRs, reinforcing the model's validation not just through numerical metrics but through human experience.

In summary, this research presents an advancement in estimating RIRs in settings with multiple sound sources. By developing a training method that focuses on a single representative RIR, we have shown that it is possible to achieve reliable predictions across various scenarios. There is still room for improvement, and future work can look at refining the model, exploring variable reflection patterns, and understanding how to estimate RIRs from individual sources.