The Corsmal Containers Manipulation Dataset Consists Of 1140 Audio Visual Inertial Recordings Of People Interacting With (15) Containers, Using 4 Cameras (Rgb, Depth, And Infrared) And An 8 Element Circular Microphone Array. The Containers Are 5 Drinking Cups, 5 Drinking Glasses And 5 Food Boxes. These Containers Are Made Of Different Materials, Such As Plastic, Glass And Paper. Containers Are Either Empty Of Filled At 2 Different Levels (50%, 90%) With 3 Different Types Of Content (Water, Pasta, Rice). For Example, People Can Pour A Liquid In A Glass/Cup Or Shake A Food Box. The Combination Of Containers And Fillings Results In A Total Of 95 Configurations Executed By A Different Subject For Three Scenarios And For Two Backgrounds And Two Illumination Conditions. The Total Number Of Configurations Is 1140. The Dataset Is Split Into Training Set (9 Containers), Public Testing Set (3 Containers), And Private Testing Set (3 Containers). The Containers For Each Set Are Evenly Distributed Among The Three Container Types. The Dataset Provides Synchronized Rgb Videos (1280x720 Pixels At 30 Hz), Narrow Baseline Stereo Infrared Videos, Depth Images, Audio Recordings, Accelerometer And Gyroscope Data, Calibration Data, And Annotations. Rgb, Infrared And Depth Data Are Spatially Aligned. Audio Signals Are Sampled Synchronously At 44.1 K Hz Using A Compact Circular Shape Based 8 Microphone Array. Moreover, Audio Signals Are Affected By Background Noise, Such As Office Noise And Outside Noise (E.G. Busy Street And Wind), As Recordings Were Performed In A University Room In Different Moments Of The Day Over A Week. Calibration Data Consist Of The Intrinsic Parameters For Each Camera, And The Location Of Each Of The Microphones With Respect To The Camera Reference System. Annotations Include The Container Capacity, Filling Type (Water, Rice Or Pasta), Filling Level, And The Mass Of The Container And Filling For The Training Set. The Dataset Is Licensed Under The Creative Commons Attribution Non Commercial 4.0 International License. published 2020 via Queen Mary University of London