Maintaining gravel roads is crucial, as loose gravel poses safety risks and increases vehicle costs. Current methods used by the Swedish road administration, Trafikverket, are subjective and time-consuming. Road agencies need a cost-effective, efficient, and unbiased approach to assess gravel road conditions. Studies show human ratings are error-prone and inconsistent. This study aims to develop an automatic method for estimating loose gravel using audio recordings from inside a vehicle, capturing the sound of gravel hitting the car's bottom. These recordings were classified into four classes based on Trafikverket regulations. Sound features were extracted and analysed using supervised machine-learning methods. The Multilayer Perceptron (MLP) achieved the highest classification accuracy of 0.96, with an F1 score, recall, and precision of 0.97. Results indicate that audio data can effectively classify loose gravel conditions.