## 1 Introduction  
Acoustic sensing has long been employed in the field of mechanical fault detection, particularly through Acoustic Emission (AE) techniques. AE refers to the passive detection of transient elastic waves generated by materials under stress, such as crack propagation, friction, or impact. It has proven to be an effective and sensitive method for identifying faults in components like bearings, gears, and induction motors, where mechanical defects naturally emit detectable high-frequency sound signals. Due to its non-invasive nature and high responsiveness, AE has been widely adopted in industrial condition monitoring systems [1, 2, 3, 4, 5, 6, 7, 8]. Furthermore, several comparative studies have suggested that AE offers superior coverage of higher-frequency bands and greater sensitivity to early-stage damage than traditional vibration-based sensors. These characteristics make it suitable for detecting minute structural changes that might otherwise go unnoticed using conventional accelerometer-based diagnostics [9, 10]. However, the effectiveness of AE-based fault diagnosis is inherently tied to the occurrence of spontaneous sound-generating events. In other words, the machine must emit a sound due to internal stress or damage for the system to detect it. This reliance on passive signal acquisition poses a limitation, especially in systems that do not continuously produce mechanical noise or operate under conditions that do not easily generate Acoustic Emissions. Robotic systems exemplify such a challenge. Unlike rotating machinery where faults naturally generate acoustic signals, robots typically produce minimal or inconsistent mechanical noise during normal operation, especially in static or idle states. This limits the applicability of conventional AE techniques for robotic fault detection. For instance, [PERSON] et al. [11] proposed a fault diagnosis system for a six-legged walking robot that utilizes onboard cameras and visual markers placed on the robot's limbs to detect structural deviations. While this method can effectively assess limb positioning and configuration under optimal conditions, it is inherently limited by occlusion--when markers are blocked or move out of the camera's field of view--and by environmental factors such as lighting and visual clutter. These constraints highlight the need for sensing methods that are robust to visual obstruction and environmental variability. To overcome these limitations, we turn to active acoustic sensing technology. Active acoustic sensing (AAS) involves intentionally sending acoustic signals (e.g., swept tones) into a system and estimating the transmission characteristics of the target object by analyzing the responses, such as reflection, resonance, and absorption patterns. Compared to vibration-based sensors, which often require direct contact with the structure and are limited to narrow frequency bands, active acoustic sensing provides the flexibility to excite and measure responses across a broader frequency range. This allows for more detailed structural characterization and increases the likelihood of detecting subtle anomalies in robot assemblies, including loosely connected parts or early-stage damage. Hand posture estimation is a typical application of AAS in the field of human-machine interaction [12; 13; 14; 15]. In many cases, the aim is to recognize gestures by wearing a ring or bracelet-like device on a finger or hand and estimating the hand posture. In recent years, AAS has been applied not only to hand posture estimation but also to estimating sitting posture [16]. It is also applied to object localization [17; 18], user authentication [19; 20] and action recognition [21]. While these applications have demonstrated the versatility of AAS in human-computer interaction, its potential for robotic structural diagnosis remains largely underexplored. This study uniquely explores the use of AAS to detect and localize structural anomalies in robots, thereby extending its application domain to autonomous mechanical systems.